Cloud computing is a novel paradigm that allows users to remotely access their data through web- based tools and applications. Later, the users do not have the ability to monitor or arrange their data. In this case, many security challenges have been raised. One of these challenges is data integrity. Contentiously, the user cannot access his data directly and he could not know whether his data is modified or not. Therefore, the cloud service provider should provide efficient ways for the user to ascertain whether the integrity of his data is protected or compromised. In this paper, we focus on the problem of ensuring the integrity of data stored in the cloud. Additionally, we propose a method which combines biometric and cryptography techniques in a cost-effective manner for data owners to gain trust in the cloud. We present efficient and secure integrity based on the iris feature extraction and digital signature.  Iris recognition has become a new, emergent approach to individual identification in the last decade. It is one of the most accurate identity verification systems. This technique gives the cloud user more confidence in detecting any block that has been changed. Additionally, our proposed scheme employs user’s iris features to secure and integrate data in a manner difficult for any internal or external unauthorized entity to take or compromise it. Iris recognition is an internal organ that is well protected against damage and wear by a highly transparent and sensitive membrane. Extensive security and performance analysis show that our proposed scheme is highly efficient and provably secure.

The current state of education is mostly electronic. Factors such as servers, storage space, and software are more prominent than ever before. Cloud Computing is defined as an Internet-based computing space that allows its users to share resources, software and information. In the context of Iraq, increased educational spending has not translated into improved learning environments. This work intends to increase the efficiency of education in Iraq through reviewing the characteristics associated with cloud computing providers, such as Microsoft, Google and Amazon, in the context of enhancing the advantages to students, teachers, and other stakeholders. The work will also try to determine approaches that offered rich and affordable services and tools through posing a suitable Cloud Computing Model for Iraqi Schools (CCIS). This particular model is made up of three major parts; preparation, implementation and monitoring, and evaluating and reviewing. The CCIS model combines public and private clouds in the provision of multiple services to the students and enables the formation of links outside of schools. Problems associated with security and data privacy are quite low and under control in this model, as they are defended beyond firewalls alongside remote services, scalability, low costs, efficiency, and functional plug and play options. This study will also decrease the challenges faced by the model internally and externally via constant appraisals and review.

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The effective selection of protein features and the accurate method for predicting protein structural class (PSP) is an important aspect in protein folding, especially for low-similarity sequences. Many promising approaches are proposed to solve this problem, mostly via computational intelligence methods. One of the main aspect of the prediction is the extraction of an excellent representation of a protein sequence. An integrated vector of dimensions 71 was extracted using secondary and hydropathy information in this study Using newly developed strategies for categorizing proteins into their respective main structures classes, which are all-α, all-β, α/β, and α+β. Support Vector Machine (SVM) and Differential Evolution (DE) were combined using the wrapper method to select the top N features based on the level of their respective importance. The classification can be made more accurate by tuning the kernel parameters for the SVM in the training phase. In this study, the mean of the classification rate from using the SVM classifier was used to evaluate the selected subset of features. This study was tested using two low - similarity data sets (D640 and ASTRAL). A comparison between the proposed (SVM + DE) based on DE feature selection approach and (SVM+DE) based on grid search (a traditional method to search for parameters) forms the core of this work. The proposed SVM+DE model is competitive and highly reliable in terms of time and performance accuracy compared with other reported methods in literature.