Nature inspires human beings to a greater extent as the mother nature has guided us to solve many complex problems around us. Algorithms are developed by analysing the behaviour of the nature and from the working of groups of social agents like ants, bees, and insects. An algorithm developed based on this is called 'nature inspired algorithms'. These nature-inspired algorithms can be based on swarm intelligence, biological systems, physical and chemical systems. A few algorithms are effective and have proved to be very efficient and thus have become popular tools for solving real-world problems. Swarm intelligence is one of the most important algorithms developed from the inspiration of group of habitats. The purpose of this paper is to present a list of comprehensive collective algorithms that invoke the research scope in that area.

The image authentication plays a vital role in modern multimedia technology. The existing technique for preserving authentication abided the content only by preserving transformations like scaling, additive noise, gamma correction, brightness adjustments, water marking etc., but it fails to authenticate larger angles rotations. Very few systems had authenticated rotations to a smaller angle which was less than 5 o . In existing system, the failure occurred in authenticating large angle rotation is due to the finite divisions of equal sized square block for calculating the local features. In this paper concept of dividing circular blocks with equal area is analyzed for better competency. The Haralick features are mostly calculated for square block. In the proposed system 14 features of Haralick has been grouped to hash code for each circular blocks in sender side. In receiver side, the same procedure is followed to generate hash code and the comparison is carried out to verify the authentication. In addition to scaling, brightness, contrast adjustment, gamma correction etc., the proposed system tolerates rotation even to greater angles up to 360 o with better efficiency.

The main objective of this paper is intrusion detection system for a cloud environment using combined PFCM-RNN. Traditional IDSs are not suitable for cloud environment as network-based IDSs (NIDS) cannot detect encrypted node communication, also host-based IDSs (HIDS) are not able to find the hidden attack trail. The traditional intrusion detection is largely inefficient to be deployed in cloud computing environments due to their openness and specific essence. Accordingly, this proposed work consists of two modules namely clustering module and classification module. In clustering module, the input dataset is grouped into clusters with the use of possibilistic fuzzy C-means clustering (PFCM). In classification module, the centroid from the clusters is given to the recurrent neural network which is used to classify whether the data is intruded or not. For experimental evaluation, we use the benchmark database and the results clearly demonstrate the proposed technique outperformed conventional methods.

This paper proposes a retrievable data perturbation model for overcoming the challenges in cloud computing. Initially, genetic whale optimization algorithm (genetic WOA) is developed by integrating genetic algorithm (GA) and WOA for generating the optimized secret key. Then, the input data and the optimized secret key are given to the Tracy-Singh product-based model for transforming the original database into perturbed database. Finally, the perturbed database can be retrieved by the client, if and only if the client knows the secret key. The performance of the proposed model is analyzed using three databases, namely, chess, T10I4D100K and retail databases from the FIMI data set based on the performance metrics, privacy and utility. Also, the proposed model is compared with the existing methods, such as Retrievable General Additive Data Perturbation, GA and WOA, for the key values 128 and 256. For the key value 128, the proposed model has the better privacy and utility of 0.18 and 0.83 while using the chess database. For the key value 256, the proposed model has the better privacy and utility of 0.18 and 0.85, using retail database. From the analysis, it can be shown that the proposed model has better privacy and utility values than the existing models.

Cloud computing serves as a major boost for the digital era since it handles data from a large number of users simultaneously. Besides the several useful characteristics, providing security to the data stored in the cloud platform is a major challenge for the service providers. Privacy preservation schemes introduced in the literature trying to enhance the privacy and utility of the data structures by modifying the database with the secret key. In this paper, an optimization scheme, Brain Storm based Whale Optimization Algorithm (BS-WOA), is introduced for identifying the secret key. The database from the data owner is modified with the optimal secret key for constructing the retrievable perturbation data for preserving the privacy and utility. The proposed BS-WOA is designed through the hybridization of Brain Storm Optimization and Whale Optimization Algorithm. Simulation of the proposed technique with the BS-WOA is done in the three standard databases, such as chess T10I4D100 K, and the retail databases. When evaluated for the key size of 256, the proposed BS-WOA achieved privacy value of 0.186 and utility value of 0.8777 for the chess database, and thus, has improved performance.

State estimation of power systems has become vital in recent days of power operation and control. SCADA and EMS are intended for the state estimation and to communicate and monitor the systems which are operated at specified time. Although various methods are used we can achieve the better results by using PMU technique. On placing the PMU, operating time is reduced and making the performance reliable. In this paper, PMU placement is done in two ways. Those are 'optimal technique with pruning operation' and 'depth of unobservability' considering incomplete and complete observability of a network. By Depth of Unobservability Number of PMUs are reduced to attain Observability of the network. Proposed methods are tested on IEEE 14, 30, 57, SR-system and Sub systems (1, 2) with bus size of 270 and 444 buses. Along with achieving complete observability analysis, single PMU loss condition is also achieved.

Cardiovascular disease remains the biggest cause of deaths worldwide and the heart disease prediction at the early stage is very important. The classification problem of assigning several observations into different disjoint groups plays an important role in business decision making and many other areas. A heart disease dataset is analysed using neural network approach. The main aim of data mining is to find relationships in data and to predict outcomes. Classification is one of the important data mining techniques for classifying given set of input data. Many real world problems in various fields such as business, science, industry and medicine, can be solved by using classification approach. For the better classification and improve the accuracy, optimisation technique is used. To optimise the weight of the ANN structure, GSO technique is used. From the classification results process, the maximum accuracy is 82.23% in heart diseases database classification process.

Cardiovascular disease is one of the major causes of death across the globe and the detection of the disease at an early stage is of utmost importance. Of late, due to the development of technology, early and accurate detection of the disease has become possible and this in turn can help in reducing the risk of the disease and in reducing the number of patients affected. At the same time, diagnosis of the disease is the most critical as well as a complicated problem which has to be accomplished in an accurate and efficient manner. Heart disease data can be analyzed using a neural network approach. The primary goal of data mining is to find the relation between data as well as predicting the result. The prime data-mining technique to classify a provided set of input data is classification. Several practical issues which we face in our day-to-day life in various multifaceted disciplines from business to medicine can be tackled by classification approach. Classification process can be made more efficient by adopting parallel approach in the training phase. Optimization technique can be used for better classification and to improve accuracy. To optimize the weight of the Artificial Neural Network (ANN) structure, Group Search Optimizer (GSO) technique is used.

The fast development of wireless sensor networks has made a chance to accumulate and remove enormous measure of data from Wireless Sensor Networks. WSN is efficient instrument that empowers its clients to nearly screen, comprehend and control application handle. WSN consist of huge number of heterogeneous sensor hub spread over the extensive territory and help for wireless sensing and data processing. Information administration and handling for wireless sensor networks (WSNs) has turned into a theme of dynamic research in few fields of software engineering, for example, the dispersed frameworks, the database frameworks, and the information mining. A wireless sensor network is made out of countless and sensors and hubs. These sensors hubs have a few limitations like data is highly resource constraints, huge in volume. Because of their asset limitations, traditional information mining strategies are not reasonable to WSN. This inspires to outline a novel and proficient information digging procedures for WSN. In this paper diverse existing information digging procedures for WSN are studied and some research challenges related to the adoption of traditional data mining techniques are listed out.

Mobile Ad Hoc Networks (MANETs) merge wireless communication with higher node mobility. Restricted wireless communication range, and node mobility requires cooperation with all others to offer networking, and with the network changing vigorously, needs are to be continually met. Routing protocols allows MANET operation. To improve the performance of the routing, various constraints such as bandwidth availability, hop count, link quality, energy are to taken into consideration during design of the routing protocol. This research paper provides a protocol for MANETs based on Dynamic Source Routing (DSR) and hybrid optimization based on Ant Colony Optimization (ACO) and Tabu search to find routes to destination with optimalhop count, link quality and bandwidth estimation.

Telemedicine is a latest IT and communication development technology application for ensuring quality and timely healthcare to people, particularly to those in critical condition. For tele- medical applications optimal performance, wireless ad hoc networks and communication tools must be optimised for medical applications to offer the required Quality of Service (QoS). Ad hoc networks are still a challenge for researchers due to its dynamic nature, limited bandwidth and power. Routing algorithms form the basis of communication. Swarm Intelligence (SI) methods like Ant Colony Optimization (ACO) protocols are based on foraging ant's behaviour. ACO is widely used to develop ad hoc networks routing algorithms based on ant's collective behaviour to locate shortest paths from nest to food source through depositing of pheromone. In this paper, the performance of wireless ad hoc telemedicine system is improved using proposed ACO based routing protocols. Outcomes of simulations demonstrate the efficiency of the suggested methods in transmitting multimedia data across the network.

In content based image authentication system the image hash is generated by extracting image features that holds the content of the image using transform coefficients, Edges, Statistical methods, Local and Global descriptors, Histogram etc. Many authentication systems employ any one of the feature extraction methods to generate an image hash. Recently most authentication system uses a combination of two or more feature extraction methods, to extract the content of image and then to generate an image hash. This is done to fully exploit the advantage of two or more methods over just a single method. In this paper a survey on most recent work in the area of content based image authentication that uses two or more feature extraction techniques and a performance analysis on each of the system based on the requirement of an authentication system is given.

Routing in ad hoc networks is challenging as nodes are mobile and links are continuously created and broken. Current on-demand ad hoc routing algorithms start route discovery after path break, incurring high cost to detect the disconnection and to establish a new route. Specifically, when a path is liable to break, the source is warned about the likelihood of disconnection. The source then starts path discovery avoiding disconnection entirely. A path is likely to break when link availability decreases. Since routing is nondeterministic polynomial (NP) hard, this work proposes an improved ad hoc on-demand multipath distance vector (AOMDV) based on link availability, neighboring node’s queuing delay, node mobility, and bit error rate. The optimal path is selected using BAT meta-heuristic optimization. Simulation shows improved performance compared to AOMDV.

The field of wireless networks is an important and challenging area. In this paper, routing in mobile adhoc networks (MANETs) using ant algorithm has been described. ANTHOCNET algorithm makes use of ant-like mobile agents which sample the nodes between source and destination. In MANET, each and every node has an additional task by which it can forward packets between two or more nodes. The routing protocol in MANET should be capable of adjusting between two or more nodes. The routing protocol should be capable of adjusting between high mobility, low bandwidth to low mobility, and high bandwidth scenario. AODV protocol is being estimated for higher throughput. By varying the time cycle, throughput and packet efficiency can be increased. When throughput is increased, efficiency is also high. This also improves the quality of services.

Wireless sensor networks face many threats which drain the energy. The performance of sensor network routing is much affected in the presence of selfish nodes with messages being delivered with a longer delay. Social network routing is a method in which the messages are selectively forwarded through the nodes where the encounters between these nodes are more likely to occur. Network reputations clearly speak about the quality of nodes involved in data forwarding. The idea is to utilise social network reputations of source or destinations for effective data forwarding in farmland sensor networks.

Ad hoc networks are wireless mobile hosts collections forming a temporary network without any infrastructure/centralized administration. So a mobile host is required to enlist the help of other hosts to forward packets to destination because of each mobile host’s limited range of wireless transmission. The paper presents a protocol for routing in ad hoc networks using Dynamic Source Routing (DSR) and Swarm Intelligence based on Ant Colony Optimization (ACO) to optimize the node pause time. The simulation results shows that the improved performance of routing in the network.