Nowadays, more people are affected by various diseases such as blood pressure, heart failure, etc. The early prediction of diseases tends to increase the survival of affected patients by allowing preventive action. A key element for this purpose is the digitalization of the healthcare system through the Internet of Things (IoT) and cloud computing. Nevertheless, there are major problems in the cloud with the IoT due to false predictions and errors in medical data, which results in taking a longer time to receive patient details and not providing the best outcome. Data transfer through the cloud can also be hacked by attackers due to the lack of security. This leads to a challenge for medical experts to predict the diseases accurately for a specific patient. Therefore, a novel hybrid elapid encryption (HEE) method was proposed for improving the security of cloud systems. In addition, the affected person’s disease and the severity risk level were predicted and classified using the proposed novel hybridization technique of the generalized-fuzzy-intelligence-based gray wolf ant lion optimization (GFI-GWALO) method. After the disease is predicted, the alert signal is provided to the patients. Moreover, this proposed research was implemented on MATLAB. Then the proposed simulation outcome was compared with various conventional methods and showed that the proposed method has the best outcomes in terms of its security and disease prediction with 80 ms of encryption time and 78 ms of decryption time, 100% accuracy, 99.50% precision and 8 ms of processing time.

We propose a fast, yet simple algorithm to find the region of interest (ROI) from a compressed MPEG video bitstream, with partial decoding. We have used the properties of the human visual system (HVS), applied in the DCT domain, to extract the ROIs. Though a lot of work has been done to obtain ROIs in images based on the HVS, all these systems work in the pixel domain. However, finding the ROIs in the compressed domain has wide applications such as emphasizing perceptually important regions while transrating, defining descriptors for encoded video (MPEG-7), changing image size to adapt to heterogeneous client displays, etc.

In metro cities, the effective and efficient management of traffic is one of the most demanding and time taking tasks. Vehicular ad hoc networks (VANET) provide unfailing, low-cost solutions for transportation systems with intelligence. VANET, a subclass of mobile ad hoc networks (MANET), allows exchange of information among vehicles and/or roadside devices. VANET can be implemented in numerous application areas such as effective traffic management, safety, and user comfort for drivers as well as passengers. It provides phenomenal growth to both in industries and research communities. Secure mobility and handoff management are the most promising and challenging research issues in VANET. In this paper, we have introduced the security in intra domain mobility handoff in PMIPv6 for VANET. Existing intra handover schemes does not include the authentication cost while evaluating the total packet delivery cost for intra domain handoff. Our proposed scheme includes the authentication cost of an intra-domain handover for evaluating the total packet delivery cost of handover for next-generation mobility management protocols, which is PMIPv6 for the vehicular network. We have considered the vital parameters such as the number of MAGs, setup cost, binding update cost, unit transmission cost for analyzing the total packet delivery cost. Furthermore, a comparative study with authentication and without authentication cost for the considered parameters shows that our proposed scheme secures the handover process with slight variation in cost.

The security of many computer systems hinges on the secrecy of a single word - if an adversary obtains knowledge of a password, they will gain access to the resources controlled by this password. For the vast majority of computer systems, passwords are the method of choice for authenticating users. The most widely and commonly used authentication is traditional “Username” and “Password”. Among the various means of available resource protection including biometrics, password based system is most simple, user friendly, cost effective and commonly used. But this method having high sensitivity with attacks. Most of the advanced methods for authentication based on password encrypt the contents of password before storing or transmitting in physical domain. In this paper we are giving such authentication credential to various techniques which is used in password based authentication and giving the techniques for prevention of password attacks.

Nowadays, all the applications have been moved to the intelligent world for easy usage and advancements. Hence, the sensed data have been utilized in the smart medical field to analyze the disease based on the symptom and to suggest controlling the disease severity rate. However, predicting the disease severity range based on the sensed disease symptom is more complicated because of the complex and vast data. So, the present work has introduced a novel Generalized approximate Reasoning base Intelligence Control (GARIC) with Ant Lion Optimization (ALO) algorithm to forecast the disease type and measure the severity range. Here, the presence of the Ant lion fitness has afforded the finest disease classification and severity analysis results. Finally, the parameters were measured and compared with other conventional models and have recorded the finest disease prediction score and severity range. This verified the success rate of the designed model in estimating the disease severity range. In addition, the presented system helps to notify the people of medical advice by message, email, or other application.