In cloud computing systems, the data is stored on remote servers accessed through the internet. The increasing volume of personal and vital data, brings up more focus on storing the data securely. Data can include financial transactions, important documents, and multimedia contents. Implementing cloud computing services may reduce local storage reliance in addition to reducing operational and maintenance costs. However, users still have major security and privacy concerns about their outsourced data because of possible unauthorized access within the service providers. The existing solutions encrypt all data using the same key size without taking into consideration the confidentiality level of data which in turn will increase the cost and processing time. In this research, we propose a secure cloud computing model based on data classification. The proposed cloud model minimizes the overhead and processing time needed to secure data through using different security mechanisms with variable key sizes to provide the appropriate confidentiality level required for the data. The proposed model was tested with different encryption algorithms, and the simulation results showed the reliability and efficiency of the proposed framework.

Cloud Computing revolves around internet based acquisition and release of resources from a data center. Being internet based dynamic computing; cloud computing also may suffer from overloading of requests. Load balancing is an important aspect which concerns with distribution of resources in such a manner that no overloading occurs at any machine and resources are optimally utilized. However this aspect of cloud computing has not been paid much attention yet. Although load balancing is being considered as an important aspect for other allied internet based computing environments such as distributed computing, parallel computing etc. Many algorithms had been proposed for finding the solution of load balancing problem in these fields. But very few algorithms are proposed for cloud computing environment. Since cloud computing is significantly different from these other types of environments, separate load balancing algorithm need to be proposed to cater its requirements. This work proposes an Autonomous Agent Based Load Balancing Algorithm (A2LB) which provides dynamic load balancing for cloud environment. The proposed mechanism has been implemented and found to provide satisfactory results.

The most important requirement in cloud computing environment is the task scheduling which plays the key role of efficiency of the whole cloud computing facilities. Task scheduling in cloud computing means that to allocate best suitable resources for the task to be execute with the consideration of different parameters like time, cost, scalability, make span, reliability, availability, throughput, resource utilization and so on. The proposed algorithm considers reliability and availability. Most scheduling algorithms do not consider reliability and availability of the cloud computing environment because the complexity to achieve these parameters. We propose mathematical model using Load Balancing Mutation (balancing) a particle swarm optimization (LBMPSO) based schedule and allocation for cloud computing that takes into account reliability, execution time, transmission time, make span, round trip time, transmission cost and load balancing between tasks and virtual machine .LBMPSO can play a role in achieving reliability of cloud computing environment by considering the resources available and reschedule task that failure to allocate. Our approach LBMPSO compared with standard PSO, random algorithm and Longest Cloudlet to Fastest Processor (LCFP) algorithm to show that LBMPSO can save in make span, execution time, round trip time, transmission cost.

Signatures are imperative biometric attributes of humans that have long been used for authorization purposes. Most organizations primarily focus on the visual appearance of the signature for verification purposes. Many documents, such as forms, contracts, bank cheques, and credit card transactions require the signing of a signature. Therefore, it is of upmost importance to be able to recognize signatures accurately, effortlessly, and in a timely manner. In this work, an artificial neural network based on the well-known Back-propagation algorithm is used for recognition and verification. To test the performance of the system, the False Reject Rate, the False Accept Rate, and the Equal Error Rate (EER) are calculated. The system was tested with 400 test signature samples, which include genuine and forged signatures of twenty individuals. The aim of this work is to limit the computer singularity in deciding whether the signature is forged or not, and to allow the signature verification personnel to participate in the deciding process through adding a label which indicates the amount of similarity between the signature which we want to recognize and the original signature. This approach allows judging the signature accuracy, and achieving more effective results.

In this letter, we propose a new adaptive weighted mean filter (AWMF) for detecting and removing high level of salt-and-pepper noise. For each pixel, we firstly determine the adaptive window size by continuously enlarging the window size until the maximum and minimum values of two successive windows are equal respectively. Then the current pixel is regarded as noise candidate if it is equal to the maximum or minimum values, otherwise, it is regarded as noise-free pixel. Finally, the noise candidate is replaced by the weighted mean of the current window, while the noise-free pixel is left unchanged. Experiments and comparisons demonstrate that our proposed filter has very low detection error rate and high restoration quality especially for high-level noise.