The lifetime of a node in wireless sensor networks (WSN) is directly responsible for the longevity of the wireless network. The routing of packets is the most energy-consuming activity for a sensor node. Thus, finding an energy-efficient routing strategy for transmission of packets becomes of utmost importance. The opportunistic routing (OR) protocol is one of the new routing protocol that promises reliability and energy efficiency during transmission of packets in wireless sensor networks (WSN). In this paper, we propose an intelligent opportunistic routing protocol (IOP) using a machine learning technique, to select a relay node from the list of potential forwarder nodes to achieve energy efficiency and reliability in the network. The proposed approach might have applications including e-healthcare services. As the proposed method might achieve reliability in the network because it can connect several healthcare network devices in a better way and good healthcare services might be offered. In addition to this, the proposed method saves energy, therefore, it helps the remote patient to connect with healthcare services for a longer duration with the integration of IoT services.

Steganography is a type of secret communication which is used to hide the confidential information from the intruder. The security during information communication is a key concern which makes image steganography a popular technique. Image steganography techniques may be broadly classified into two domains i.e., spatial domain and frequency domain. This paper proposes an improved image steganography technique in which data is embedded in the edge pixels of the carrier image. An intruder has less suspicion about the existence of data bits in edges, because edge pixels appear to be different than their neighbours and thus ensures better security. The proposed technique uses different types of edge detection filters like Prewitt, Sobel, Laplacian and Canny in the existing image steganography using edge based data hiding in DCT domain algorithm. Furthermore, the proposed technique results in excellent performance with PSNR of 99% and SNR of 96%. However, the proposed steganography technique surprisingly results in reduction of image size mainly because of image compression.

A challenging task for the medical science is to achieve the accurate diagnosis of diseases prior to its treatment. A pattern classifier is used for solving complex and non-separable computing problems in different fields like biochemical analysis, image processing and chemical analysis etc .The accuracy for thyroid diagnosis system may be improved by considering few additional attributes like heredity ,age, anti-bodies etc. In this paper, a thyroid disease prediction system is developed using multilayer perceptron (MLP). The proposed system uses 7–11 attributes of individuals to classify them in normal, hyperthyroid and hypothyroid classes. The proposed model uses gradient descent backpropogation algorithm for training the multilayer perceptron using dataset of 120 subjects. The thyroid prediction system promises excellent overall accuracy of ~100% for 11 attributes. However, the system results in a lower accuracy of 66.7% using 11 attributes and 70% using 7 attributes with 30 subjects.

Biometrics is the science of human recognition by means of their biological, chemical or behavioural traits. These systems are used in many real life applications simply from biometric based attendance system to providing security at a very sophisticated level. A biometric system deals with raw...