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One of the major problems that world is facing today is waste management. Most of the times it is seen that the waste is been spilled over the bins and lying nearby. This leads to spreading of some deadly diseases in the surrounding environment. Also, people find it difficult to walk beside with it. All these problems are due to lack of coordination and communication among the garbage team members. Also the study has revealed that the waste management can be far more efficient if the garbage is segregated at source and then disposed in dumping grounds separately. Thus, there is a big need to have a proper waste management.Considering the above issues, this paper proposes a system, that will try to reduce these problems to a greater extent. This system initially segregates the waste and then monitors the garbage level in bins using IoT. This data related to bin level is sent to a server over internet, where the system processes the real time data and raises alerts to manage the collection of Waste. The proposed system also takes care of long time goal of identifying the pattern of waste generation at various localities. This data collected by the system can be used to further plan the effective measures to reduce the waste mismanagement and to maintain cleaner environment. General TermsInternet of Things, Clean Environment

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The liver is a vital organ in human body, and Liver Tumor is considered to be a fatal disease. The tumors which can occur in Liver are cancerous or non-cancerous. For diagnosis of tumor, detection and demarcation of tumor is the initial step to be performed. After detection of the tumor, its type can be determined by using technique like biopsy, which is an invasive technique. To avoid such an invasive diagnosis technique, Non invasive techniques like diagnosis based on Medical Images using a CAD system can also be used. In such CAD systems, Detection and Segmentation of Tumor is performed automatically or semi-automatically. In this work, a system is developed to perform Segmentation of the Liver Tumor from abdominal CT image. This system segments the tumor in the two level operation. The first level of operation is segmentation of Liver from abdominal CT image and the second level is segmentation of Tumor from the result of first level. Segmentation of Liver is performed by using two methods namely Adaptive Thresholding with Morphological operations and Global Thresholding with morphological operations. Whereas segmentation of Tumor is performed by using three methods namely Adaptive Thresholding with Morphological operations, Fuzzy C Mean Clustering and Region Growing. This segmentation application generates and compares the outcomes of these implemented techniques. The system compares and selects the best of all the Tumor segmentation results and produces the final result. In this work, a robust system is proposed, by improving the accuracy of the segmentation for distinct quality and category of abdominal CT images, which contain liver tumors.