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Diabetic retinopathy is an eye condition that can cause vision loss and blindness in people who have diabetics. It affects blood vessels in the retina. Initially, Diabetic retinopathy may not have any symptoms, but finding it early can help us to take steps to protect our vision. Some people notice changes in their vision, like trouble in reading or seeing faraway objects, these changes may come and go. In later stages of diseases, blood vessels in the retina starts to bleed into the vitreous. If this happens, you may see dark, floating spots or streaks that look like lobwels. Sometimes the spots clear up on their own, but it is important to start the treatment, otherwise it may get worse and the bleeding can happen again. There are various stages, it includes blurred vision, impairment of color vision, floaters, patches or streaks. Hence in our project, we came up with an idea of identifying diabetic retinopathy in early stages, to classify a given set of images into four classes, we are using supervised learning methods. For this task, we use deep learning technique with inception v3module along with skin locus model in order to achieve better results and for easy classification of images

Millions of individuals worldwide are infected by a serious disorder known as Dementia with collection of symptoms which will embody blackout and feel inconvenient to think, solve problems. Dementia is controllable if the person is within the drafting board of not wellness, so as to help such dementia affected people, the target is to style sensible smart wearable device that motivates and enthusiast their life easier. The good device practises on 3 operations. At first the routine activities are monitored by the sensors and trained using a machine learning algorithms which are fed to a smart device based on the data, it guides the person with physical activities. Then an indoor tracking of a person is carried out by beacon signals through which if person forgets to do next activity, smart device guides them by learning the patterns of a person about their movements in indoor. At last an outdoor tracking of a person is carried out using GPS signals enabled in a smart device which helps the person to find their way to home even when they missed the way or suffers from memory loss. If any different texture or feedback is not received from the patient to any of the above mentioned activities the smart device alerts the caretaker in the form of message through app. Thus, this smart device reduces the burden of the caretaker

Nowadays, a massive amount of data leads to cause network traffic and inflexible mobility in future mobile networks. A new Group Mobility Model (GMM) named MoMo is introduced that addresses the issue of the aforementioned problems. Even though, software defined network (SDN) is functional with network-rooted mobility protocols that enhance the network efficiency. Some existing network-rooted mobility administration methods still undergo handover delay, packet loss, and high signaling cost through handover processing. In this research work, SDN-based fast handover for GMM is proposed. Here, the neighbor number of evolving node transition probabilities of the mobile node (MN) and their obtainable resource probabilities are estimated. This makes a mathematical framework to decide the preeminent number of the evolving nodes and then allot these to mobile nodes virtually with all associations finished by the exploit of Open-Flow tables. The performance examination demonstrates that the proposed SDN rooted GMM technique has the enhanced performance than the conventional handover process and further technique by handover latency, signaling cost, network throughput, and packet loss.

Agriculture is the back bone of India and nearly 70% of people in our country depend on agriculture. The yield of agriculture should be increased rapidly to fulfill the food requirements of population throughout the world. Now days Wireless Sensor Network (WSN) used for solving many real time problems. WSN plays vital role in many field like transport, medical, military, mobile phones, home appliances and so on. Agriculture is one of the important sources for all living things. But nowadays agriculture crops are affected due to many environmental changes. To overcome this WSN takes important role in the field of agriculture. In agriculture WSN used for monitoring, measuring temperature, irrigation system, measuring water supply and so on. WSN helps the farmer to produce the crop with high quantity and reduce the cost of yield. Agriculture gets affected by climatic change, environmental change, and natural disaster. Using WSN the soil and water management can be done. Here wireless sensors are used so the cost of implementation is very low. In this paper wireless sensor nodes are used to monitor the crops. The temperature, humidity and some other theft detection can be made using sensors. This helps to increase the productivity of agriculture. The human effort is reduced by automatic process and it encourage the farmer to develop the farm land. The location of the farm land can be send using GPS. Some components like sensor, Wi-Fi, camera and some other devices are used to make the agriculture as smart. All the gathered information are stored in memory or in cloud.

Security insurance against mining calculations, an advancing exploration territory, finds the pessimistic symptoms of information mining techniques that start from the protection dissemination of people and associations. Late research in security insurance information mining has focused on finding a legitimate soundness between data protection and learning revelation. In this paper, we present a calculation that creates a disinfected database which keeps up the best possible harmony between data security and information revelation. We initially create the significant sicknesses from the first database, at that point convert the first database into a cleaned (sanitized ) database through our proposed arbitrary cleaning(sanitization) process. Next, we assess the produced cleaned database based on Knowledge Discovery (KD) and Privacy Factor (PF). This assessment procedure computes the KD and PF by looking at the noteworthy illnesses created from the first database versus the sterilized database. On the off chance that the estimations of KD and PF are fulfilled by the client determined limit esteem, the sterilized database is prepared for distributing, else our proposed calculation rehashes the sterilization procedure until the KD and PF qualities are fulfilled. At long last, our proposed calculation is assessed by contrasting it and comparative well known calculations on the criteria of KD and PF. The trial results demonstrate that our proposed calculation performed well at 77% running time contrasted with comparative prevalent calculations. The memory prerequisite for our proposed calculation is 25%, while past calculations need 31% memory. Our proposed calculation additionally performed 53% superior to past calculations regarding number of critical sicknesses influenced by the proposed cleansing procedure.