Rice blast disease is the major problem in all over the world of agriculture sector. The early detection of this disease will prevent the huge economic loss for the farmer. This paper proposes a machine learning algorithm to find the symptoms of the disease in the rice plant. Automatic detection of plant disease is carried out using machine learning algorithm. Images of healthy and blast disease affected leaves are taken for the proposed system. The features are extracted for the healthy and disease affected parts of the rice leaf. The total data set consists of 300 images and divided for training and testing purposes. These images are processed with the proposed method and the leaf is categorized as either infected or healthy. The simulation results provide an accuracy of 99% for the blast infected images and 100% for the normal images during the training phase. The testing phase accuracy is found to be 90% and 86% for the infected and healthy images respectively.

The world needs to be concerned about the women around and treat them the way they deserve to be treated. Despite having so many laws for women, it doesn’t stop thieves, assaulters or molesters to abuse women. This paper suggests a smart shoe that not only helps women take care of themselves but also help them be fearless. This project makes use of GPS, GSM modules, a shock circuit and camera, that are interfaced with Raspberry Pi board and Arduino. Women facing any troubles or in any kind of danger, can immediately make use of this device, embedded in their shoe to escape from the dangerous situation and even harm the attacker.

Out of many diseases in medical science one of the rigorous problems, the World is facing today is Brain Tumor. Brain Tumor is an uncontrollable cell proliferation in the brain. Tumor can be detected using medical imaging techniques like Magnetic Resonance Imaging (MRI), But drawback is it cannot detect below 3 mm size. Hence for accurate analysis of Brain tumor, Segmentation is one of the tools that are extensively used in Medical Science. Segmentation is one of the methods to partition an image into regions. Segmentation discriminates healthy tissues from affected tissues. This makes easier for quantative analysis, accurate disease diagnosis, detection and classification of Brain tumor. As already lot of tumor segmentation methods are available, still more research should take place in this area. The reason is these MRI image display complex characteristics like high diversity in appearance of tumor and unclear tumor boundaries. Hence judicious disease diagnosis with scientific proof is essential. So that early planning can be done to save the life of patient. To facilitate the researchers working in this field, a review on various Brain Tumor segmentation is presented. The study emphasises the greater confrontation we come across while tumor Segmentation on various methods in the literature, as well. It is trusted that the methodologies and experiments put forth in this study will be of great use for researchers working in this field.

This paper addresses one of the RPL routing attacks - Rank attack, which is a fundamental security attack on the routing of in IoT networks. Today, most devices, which are used for health care, connected vehicles and smart buildings have the capability to be connected to the internet through wireless. The IoT devices use routing protocols such as RPL and 6LoWPAN. The major challenge within IoT is that the devices are resource constrained, the communication links are low power and lossy, which makes it more vulnerable to attacks. This paper presents a detailed investigation on the various algorithms developed to detect the rank attack in IoT. An Intrusion detection system (IDS) is designed for IoT, which is capable of detecting Rank attack and attacker node. This system is used to identify the Rank attack, attacker nodes based on the location information of node rank, neighboring node rank and its respective rank value, usual signal strength. The plan of this system is helping in securing the IoT network and prevent from some attacks [2]. However, applying usual IDS techniques to IoT is difficult due to its particular characteristics such as constrained-resource devices, specific protocol stacks. In this paper, present a survey of IDS research efforts for IoT. Main objective is to identify rank attack and future research possibilities to detect rank attack by using IDS Technique.

In today’s world of smart living, the smart laptop bag, presented in this paper, provides a better solution to keep track of our precious possessions and monitoring them in real time. As the world moves towards a much tech-savvy direction, the novel laptop bag discussed here facilitates the user to perform location tracking, ambience monitoring, user-state monitoring etc. in one device. The innovative design uses cloud computing and machine learning algorithms to monitor the health of the user and many parameters of the bag. The emergency alert system in this bag could be trained to send appropriate notifications to emergency contacts of the user, in case of abnormal health conditions or theft of the bag. The experimental smart laptop bag uses deep neural network, which was trained and tested over the various parameters from the bag and produces above 95% accurate results.