India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into orientation in the farming sector to decide the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and the Hadoop file system. In the proposed model (EMLRM) first, we stored the unstructured weather data in hadoop distributed file system (HDFS), process that stored data by using MapReduce Algorithm and build the rainfall prediction model by utilizing Multiple Linear Regression.We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. The experimental outcomes show that the EMLRM provided the lowest value of Root Mean Square Error (RMSE= 0.274) and Mean Absolute Error (MAE= 0.0745) compared with existing methods. The results of the analysis will help the farmers to adopt effective modeling approach for predicting long-term seasonal rainfall.

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Melanoma, a lethal form of skin cancer, poses a significant risk to global health if not detected and treated promptly. Its early detection is pivotal in increasing the likelihood of successful treatment and patient survival. However, the accurate diagnosis of melanoma remains a challenge, even for seasoned dermatologists. Consequently, there has been a growing interest in leveraging Machine Learning (ML) algorithms to augment the accuracy of melanoma diagnosis. Typically, melanoma is identified through dermoscopic imaging. Numerous previous studies have proposed the automated analysis of skin lesions using both traditional classification techniques and deep learning models. These analyses often involve the feeding of designed functions into traditional categorization systems. Nonetheless, the high visual similarity between different skin lesion types and the complexity of skin diseases often renders manual features insufficiently discriminative, leading to failure in various scenarios. Recent research suggests that convolutional networks with short connections between layers near the input and the output can be deeper, more precise, and more efficient in training. This paper adopts this approach and introduces the application of Hadoop's HdiDenseNet techniques. DenseNets offer several notable advantages: they alleviate the vanishing-gradient problem, enhance feature propagation, encourage feature reuse, and substantially reduce the number of parameters. The performance of our proposed architecture is evaluated against four highly competitive benchmark object identification challenges using a dataset comprising over 40,000 images sourced diversely. The results demonstrate that the most effective method is a densely connected distributed convolutional network, particularly when applied to patient metadata. Ultimately, this paper aims to contribute to the field of medical image analysis and potentially enhance the accuracy of melanoma diagnosis. By doing so, it could play a crucial role in improving patient prognosis and saving lives.

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Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methodology: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results & Conclusions: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.