Abstract:HealthCare has been a surging need in India. The inception of Smart India, Smart Villages has further emphasized the need of a Smart & Healthy India. However little reflection of such digitally empowered country has been seen in the field of healthcare. This has further increased the importance of the definition of ubiquity of information and computing technology in healthcare that demand constant surveillance and vigilance of healthcare to predict epidemic outbreak and biological attack. The paper’s primary focus is to analyze and determine the spread of diseases and epidemic in cities/villages. And using this analysis to predict where the next outbreak of epidemic will be. This prediction helps the health authorities to take necessary action in terms of assuring that sufficient resources are available to suffice the need and if possible stop the occurrence of such epidemic by taking necessary actions. To achieve this, we use deep neural network as the heart of our prediction. It receives its training from the past experiences of data which we have collected from hospitals and our spread network. Using this training with our dynamic data it makes predictions as well as adaptively learns from the real time data.

Keywords:epidemic prediction, deep learning, recurrent neural network, machine learning, disease spread network

Abstract:Agriculture has become much more than simply a means to feed ever growing populations. It is very important where in more than 70% population depends on agriculture in India. That means it feeds great number of people. The plant diseases affect the humans directly or indirectly by health and also economically. Identification of the plant diseases is a very vital process to avoid the losses in both quality and quantity of leafs in agricultural production system. This Project deals with digital image processing techniques for detection, processing and identification of plant diseases. Diseases can affect at any part of plant especially in leaf. This Project includes only those methods to identify disease in leaves. Disease symptoms will be visible on leaves. It is very tough job to monitor the plant diseases manually. Manual plant disease monitoring system needs more processing time and expertise in the plant disease. So a fast, automatic and accurate approach to identify the plant diseases is needed. Hence, image processing techniques are used for the detection, processing and identification of plant diseases because these techniques are fast, automatic and accurate. Traditional method of checking diseases in plants is through visualization but this method is not so relevant in detecting the diseases associated with plants. So we can provide a better alternative, fast and accurate detection by using image processing techniques which can be more reliable than some other old methods

Keywords:plant, disease, SVM, image processing, clustering, segmentation

Abstract:Image segmentation is a process of partitioning a digital image into N regions. In today’s world, Brain tumor detection using image segmentation is fundamental but challenging problem in field of computer vision and image processing due to the diverse image content, image noise, non-uniform object texture and other factors. Accurate detection of size and location of brain tumor plays a vital role in the diagnosis of brain tumor. There are many image segmentation methods available for medical image analysis but the Region based and Clustering techniques are efficient, fast and accurate. This paper presents the two efficient image segmentation algorithms i.e. K-means and region growing techniques for brain MRI images and compare the two algorithms and determine the best one.

Keywords:MRI,Image Segmentation,K-Means,Region growing

Abstract:Internet of Things (IoT) is an evolving technology in recent years. IoT serves the purpose of interlinking everyday objects, where objects are equipped with identifying, sensing and limited computing capabilities. Considering demand in fast-paced IoT environment, building a system from scratch is not always suggestible. Such needs have created many formal & informal open source technology forums where professionals can contribute in the improvement of technologies. Delivering a ‘standard solution’ in ‘less time’ are the two most attractive advantages which shall be achieved by utilizing open source technologies. In this paper we have highlighted the growth of IoT market while focusing on the value offered by open source technologies in IoT infrastructure. Along with the brief study of selective open source technologies in IoT.

Keywords:IoT devices, IoT Market, Open Source technologies, OSS

Abstract:The problem with Cyber security is continuously evolving nature of security threats. The traditional approach focused on high demand resources ignoring less important components at dangerous risks. Such an approach is insufficient in current environment and biggest example of this is WannaCry Ransomware attacks. This article highlights different characteristics of security threats. The globe has experienced a Ransomware cyber-attack “WannaCry” that has infected thousand computers worldwide. WannaCry has ability to spread through network by exploiting a critical vulnerability. This article also highlights preventive steps against the attack. Finally a formula is discussed to enhance Network Security.

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