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With the evolution of Internet standards and advancements in various Internet and mobile technologies, especially since web 4.0, more and more web and mobile applications emerge such as e-commerce, social networks, online gaming applications and Internet of Things based applications. Due to the deployment and concurrent access of these applications on the Internet and mobile devices, the amount of data and the kind of data generated increases exponentially and the new era of Big Data has come into existence. Presently available data structures and data analyzing algorithms are not capable to handle such Big Data. Hence, there is a need for scalable, flexible, parallel and intelligent data analyzing algorithms to handle and analyze the complex massive data. In this article, we have proposed a novel distributed supervised machine learning algorithm based on the MapReduce programming model and Distance Weighted k-Nearest Neighbor algorithm called MR-DWkNN to process and analyze the Big Data in the Hadoop cluster environment. The proposed distributed algorithm is based on supervised learning performs both regression tasks as well as classification tasks on large-volume of Big Data applications. Three performance metrics, such as Root Mean Squared Error (RMSE), Determination coefficient (R2) for regression task, and Accuracy for classification tasks are utilized for the performance measure of the proposed MR-DWkNN algorithm. The extensive experimental results shows that there is an average increase of 3% to 4.5% prediction and classification performances as compared to standard distributed k-NN algorithm and a considerable decrease of Root Mean Squared Error (RMSE) with good parallelism characteristics of scalability and speedup thus, proves its effectiveness in Big Data predictive and classification applications.

Epidemic diseases are the contagious diseases that have the power to unfold into the entire nation if its contagion measuring had reached the level of an outbreak and also manage to wipe out the majority of the population. There are some common and well-known epidemic disease outbreaks that were happened in the entire world such as malaria, dengue, swine flu, yellow fever, chicken pox, cholera, diphtheria, bird flu, influenza, and many more. Infectious disease surveillance is one of the most comprehensive processes during which information or data on such infectious disease disseminations, vectors and outbreaks are collected, analyzed, and interpreted in a consistent and systematic manner. Moreover, the outcomes and results ought to be distributed to people in a rapid way so as to have command and control over preventing infectious disease. Subsequently, to handle situations in real time, it is significant and important to create an infectious disease prediction model. The ultimate aim is to create a model using LSTM (Long Short Term Memory) wherein the predictive analysis is demonstrated to be effective in figuring out the epidemic disease infected location and its severity.

In the domain of medical image analytics, especially in nuclei cell image analysis, the major challenges associated with it is the availability of smaller amount of interpreted samples of images and the inability to construct a machine learning model using deep neural network. The classification performance of the deep neural network model trained with smaller number of images is very low. To improve the classification performance of the model, the dataset size is increased by using the Generative Adversarial Network (GAN) with the integration of masking bits. It generates the images that remain more similar to the original nuclei cell images present in the dataset with various logical operations along with masking bits. Moreover, GAN contains two layers such as generator layer and the discriminator layer. The generator layer takes the image as an input and produces the images that remain most comparable to the unique Nuclei images. The discriminator layer takes input as original data and the generated Nuclei cell images from the generator to predict whether the generator nuclei cell images belong to original dataset or not. Finally, the classification performance of the machine learning model is measured by using the convolution neural network (CNN) architecture.

The Covid-19 Corona Virus, also known as SARS-CoV-2, has wreaked havoc around the world, and the condition is only getting worse.It is a pandemic disease spreading from person-to-person every day. Therefore, it is important to keep track the number of patients being affected. The current system gives the computerized data in a collective way which is very difficult to analyze and predict the growth of disease in a particular area and in the world. Machine learning algorithms can be used to successfully map the disease and its progression to solve this problem. Machine Learning, a branch of computer science, is critical in correctly distinguishing patients with the condition by analyzing their chest X-ray photographs. Supervised Machine learning models with associated algorithms (like LR, SVR and Time series algorithms) to analyze data for regression and classification helps in training the model to predict the number of total number of global confirmed cases who will be prone to the disease in the upcoming days. In this proposed work, the overall dataset of the world is being collected, preprocessed and the number of confirmed cases up to a particular date are extracted which is given as the training set to the model. The model is being trained by supervised machine learning algorithms to predict the growth of cases in the upcoming days. The experimental setup with the above mentioned algorithms shows that Time series Holt's model outperforms Linear Regression and Support Vector Regression algorithms.

Neural network is widely used nowadays as it shows great result in solving the data classification and regression problems. The neuron is a basic unit in the artificial neural networks, which are strongly connected to each other by means of synaptic weights. Convergence speed of the network model depends on various factors such as weight initialization, learning rate, learning algorithm, batch size etc. This article is about to reduce the training time of weight initialization during training process of the neural network has been done with the help of cuckoo search optimization algorithm which is one of the mostly used Meta heuristic algorithms. The performance of network with various dataset using the proposed method for weight initialization is compared with other initialization methods. The effect of synaptic weights initialization using the heuristic algorithm has shown better convergence over other techniques.

Myocardial infarction (MI) which causes the damage to heart muscles and it lead to the critical stage of death. However the efficacious diagnosis of myocardial infarction (heart attacks) is needed for the healthy life of human. Electrocardiogram (ECG) is utilized to diagnose MI. A genuine time signal provides the electrical activities that are the subsidiary information about the functioning of heart. The expeditious and precise diagnose of MI need to be done with artificial intelligence based on computer aided techniques. In this paper, a multi layer deep convolutional neural network structure is proposed along with the data augmentation technique for the prediction of myocardial infarction. Furthermore, the implementation is done by using GPU version. When it comes to training and developing the new models and algorithms, the performance is determined by means of training and testing speed. Since GPU processor have been used to increase the computations speed and it also scales better then CPU.

In order to overcome this threat imposed by weeds in agriculture, a measure is taken to identify the weeds that grow along with the seedlings with the help of deep learning (DL) technique. Convolutional neural network (CNN), a class of DL render a good way to identify the weeds that harm the plant’s growth. Aiming at achieving a greater accuracy, the models such as four convolution layered, six convolution layered, eight convolution layered and thirteen convolution layered architecture were built. Comparatively, eight convolution layered architecture resulted with 97.83% as training accuracy and 96.53% of validation accuracy than the VGG-16 model resulted with. The use of CNN architectures paved way to reach training accuracy of 96.27% and validation accuracy with 91.67% in ZFNet and 97.63% as training accuracy and 92.62% of validation accuracy in ALEXNET. Therefore, by the use of this technology and suggested method there is a lot of possibilities to avoid the manual field work of identifying the weeds. Our results suggest that more of datasets can be used and fine-tuning of parameters can be done.

Problem statement: To Improve the quality of clustering; a Multi-Lev el Clustering (MLC) algorithm which produces a most accurate cluster wi th most closely related object using Alternative Decision Tree (ADT) technique is proposed. Approach: Our proposed method combines tree projection and condition for clustering formation a nd also is capable to produce a customizable cluste r for varying kind of data along with varying number of cluster. Results: The experimental results shows that the proposed system has lower computational complexity, reduce time consumption; most optimize way for cluster formulation and clustering quality compared is compared effectively. Conclusion: The new method offers more accuracy of cluster dat a without manual intervention at the time of cluster formation. Compared to existing clu stering algorithms either partition or hierarchical , our new method is more robust and easy to reach the solution of real world complex business problem.

Microarray is a high throughput technology that allows uncovering of thousands of genes concurrently. To conduct any biological test like disease prediction and classification in medical field, among the large amount of genes presented in gene expression data, only some particular amount of genes is effective for performing diagnostic test. A Supervised attribute clustering is used to find such initial co-expressed gene groups of clusters whose joint expression is strongly related with the class labels. The Mutual Information incorporates the information of sample categories to measure the similarity between attributes by sharing the information between each attributes. Thus the redundant and irrelevant attributes are eliminated. After forming the clusters, the GA is used to find the optimal feature so as to increase the class separability. Using this method, the diagnosis can be made easier and effective. The predictive accuracy is estimated using three classifiers such as K-nearest neighbors, naive bayes and Support Vector machine. Thus the overall approach provides excellent predictive capability for accurate medical diagnosis.