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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Using enhanced ant colony optimization, this study proposes an efficient heuristic scheduling technique for cloud infrastructure that addresses the issues with nonlinear loads, slow processing complexity, and incomplete shared memory asset knowledge that plagued earlier resource supply implementations. The cloud-based planning architecture has been tailored for dynamic planning. Therefore, to determine the best task allocation method, a contentment factor was developed by integrating these three objectives of the smallest waiting period, the extent of commodity congestion control, and the expense of goal accomplishment. Ultimately, the incentive and retribution component would be used to modify the ant colony calculation perfume-generating criteria that accelerate a solution time. In particular, they leverage an activity contributed of the instability component to enhance the capabilities of such a method, and they include a virtual desktop burden weight component in the operation of regional pheromone revamping to assure virtual computers’ immense. Experiences with the routing protocol should be used to explore or demonstrate the feasibility of our methodology. In comparison with traditional methods, the simulation results show that the proposed methodology has the most rapid generalization capability, and it has the shortest duration of the project, the most distributed demand, and the best utilization of the capabilities of the virtual computer. Consequently, their hypothetical technique of optimizing the supply of resources exceeds world competition.

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The satellite communication is embellished constantly by providing information, ensuring security, and enables the communication among huge at a particular time efficiently. The satellite navigation helps in determining the people’s location. Global development, natural disasters, change in climatic conditions, agriculture crop growth, etc., are monitored using satellite observation. Hence, the satellite includes detailed information data, and it must be protected confidentially. The field of the satellite is enhanced at an astonishing pace. Satellite data play an important role in this modern world; hence, the onboard-satellite data must secure through the proper selection of error detection and estimation schema. Lightweight deep learning algorithm based on Extended Kalman Filter (KFK) is proposed to detect and estimate onboard pointing error such as an error in attitude and orbit. The Extended Kalman Filter (EKF) is widely used in the satellite system. EKF is utilized in this proposed model to detect the onboard pointing error such as attitude and orbit determination. An autonomous estimation of orbit position is possible through space-borne gravity. The information obtained through the observation of satellite data is compared with the accurate gravity model in detecting the error. The utilization of EKF reduces the dependence of the ground tracking system in satellite determination. The orbital altitude and orbital position are the most important challenges faced in the satellite determination system. The satellite model using the Extended Kalman Filter is an optimum method in estimating the orbital parameters. The errors in the linearization process are detected, and this can be overcome through the proper selection of linear expansion point with the EKF algorithmic model with the Jacobian matrix calculation. The results show that the EKF implementation helps in attaining better accuracy than other methodologies. Its contribution is enormous to many space missions, autonomous rendezvous and docking for manned and unmanned missions (e.g., ISS operations and beyond, in-orbit servicing, and in-orbit refueling), routine satellite OD operations, orbital debris removal systems, Space Situational Awareness (SSA) operations, and others.

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Automatically segmenting the liver is a challenging process, and segmenting the tumour from the liver adds another layer of complexity. Because of the overlap in intensity and fluctuation in location and form of soft tissues, segmenting the liver and tumour from abdominal Computed Tomography (CT) images merely based on grey levels or shape is very undesirable. To address these challenges, this study proposes employing Gabor Features (GF) and three distinct machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), and Deep Neural Net, a more efficient way of liver and tumour segmentation from CT images (DNN). The texture data produced by GF should be consistent and homogeneous across numerous slices of the same organ. In the first, pixel level features are extracted using an array of Gabor filters. Second, utilising three separate classifiers: RF, SVM, and DNN trained on GF, liver segmentation is conducted to remove liver from an abdominal CT picture. Finally, using GF and the same set of classifiers, tumour segmentation is performed on the segmented liver image.

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