A Recommender System (RS) is the most significant technologies that handle the information overload problem of Retrieval Information by suggesting users with correct and related items. Today, abundant recommender systems have been developed for different fields and we put an effort on collaborative filtering (CF) recommender system. There are several problems in the recommender system such as Cold Start, Synonymy, Shilling Attacks, Privacy, Limited Content Analysis and Overspecialization, Grey Sheep, Sparsity, Scalability and Latency Problem. The current research explored the privacy in CF recommender system and defined the perspective privacy attributes (user's identity, password, address, and postcode/location) which are required to be addressed. Using the base models as Homomorphic and Hash Encryption scheme, we have proposed a hybrid model Homomorphic Hash Encryption (H2E) model that addressed the privacy issues according to defined objectives in the current study. Furthermore, in order to evaluate the privacy level, H2E was implementing in medicine recommender system and compared the consequences with existing state-of-the-art privacy protection mechanisms. It was observed that H2E outperform to other models with respect to determined privacy objectives. Leading to user's privacy, H2E can be considered a promising model for CF recommender systems.

In the scientific work is presented the development of a method of selecting cloud computing platforms for servicing government agencies, which in future will form the Digital Government according to the Message of the President of the Republic of Qazaqstan N. Nazarbayev to the people of Qazaqstan «Qazaqstan way - 2050: Unified goal, common interests, common future». And also in the article are considered forecasts of growth and the development of cloud services in various countries, including Qazaqstan. Based on the results of research of company «Boston Consulting Group» (BCG), reflected in the article «Qazaqstan on the way to the digital economy», according to the level of digitalization the economy of Qazaqstan occupies the 50-th ranking of 85 governments and is in a group with emerging digital economy. The digital divide between the leading governments and the lagging countries is increasing from year to year; therefore, the article reflects one of the ways to develop the digitalization of Qazaqstan's economy. Descriptions and characteristics of various companies included in the international rating of Cloud100 are described as the most successful and competitive companies in the world on cloud technologies. All companies from this list are distributed as the top 20 companies for PaaS services. A criterion for selecting companies of cloud services is proposed, taking into account international and national ICT requirements. The web-portal (www.cloud.kz) was developed to assess the quality of cloud services from various companies of the world, which are working on cloud-based services. The analysis of the introduction of cloud services in Qazaqstan, and their advantages, and benefits for the public sector was made. For the cloud-based service of the module PaaS are selected the most optimal and suitable companies from the Cloud100 list.

In the domain of knowledge, there is a rising demand for such a System to provide learning support via a platform which can generate any sort of questions automatically from provided source either (PDF) books or simply any keyword against a user needs to perform a test where STMS serves the purpose. Regarding Keyword operation, the System scraps all the text from Wikipedia and converts it into multiple choice questions. Moreover, it summarizes raw text from Wikipedia and parse the text from provided content to generate Multiple-Choice Questions(MCQs). The System also finds all the Named Entities and POS (Parts of speech tags) in the content to create relevant questions. The questions include Multiple-Choice Questions(MCQs), Cloze based questions and WH- questions (why, where, when etc.).
In addition, when users score standard points in the test then they qualify for earning zone where they can earn money ($ Dollars) for scoring points in each test. The Income comes from AdSense applied on the website and other Local ads, Affiliating marketing and advertisements. All in all, the System would help in educational learning by providing helping material in the lacking knowledge areas after analyzing the tests users have performed while the Web-Traffic is the key to Success for monetary benefits.

The implementation of the concept of building an information society implies a widespread introduction of IT in all areas of modern society, including in the field of science. Here, the further progressive development and deepening of scientific research and connections presuppose a special role of e-science. E-science is closely connected with the innovative potential of IT, including the Internet technologies, the Internet of things, cyber-physical systems, which provide the means and solutions to the problems associated with the collection of scientific data, their storage, processing, and transmission. The integration of cyber-physical systems is accompanied by the exponential growth of scientific data that require professional management, analysis for the acquisition of new knowledge and the qualitative development of science. In the framework of e-science, cloud technologies are now widely used, which represent a centralized infrastructure with its inherent characteristic that is associated with an increase in the number of connected devices and the generation of scientific data. This ultimately leads to a conflict of resources, an increase in processing delay, losses, and the adoption of ineffective decisions. The article is devoted to the analysis of the current state and problems of integration of cyber-physical systems in the environment of e-science and ways to effectively solve key problems. The environment of e-science is considered in the context of a smart city. It presents the possibilities of using the cloud, fog, dew computing, and blockchain technologies, as well as a technological solution for decentralized processing of scientific data.

Managing the underlying structure of an enterprise is a daunting task. The business management and IT management alike have to deal with intricate layers of complexity that lies beneath the surface of the day-to-day operations of an enterprise. Without a proper Enterprise Architecture Framework, any organization regardless of size and magnitude of operations is bound to struggle in managing their business strategies. However, choosing a suitable Enterprise Architecture Framework is in itself a pretty hard endeavor that requires a deep dive into the terrifying maze of available Enterprise Architecture Frameworks and their respective characteristics. In this study, we compare the major Enterprise Architectu¬re Frameworks that are currently prevalent in Pakistan. Through a well-crafted questionnaire we conducted a survey and assessed what Enterprise Architecture Frameworks most of the industries in Pakistan are using and the enterprise’s level of satisfaction with the achieved results. By focusing on the trends of Enterprise Architecture Framework implementation in Pakistan we try to offer a unique perspective on the comparative studies of Enterprise Architecture Framework that are usually done on general basis.

Software defect prediction is one of the emerging research areas of software engineering. The prediction of defects at early stage of development process can produce high quality software at lower cost. This research contributes by presenting a feature selection based ensemble classification framework which consists of four stages: 1) Dataset selection, 2) Feature Selection, 3) Classification, and 4) Results. The proposed framework is implemented from two dimensions, one with feature selection and second without feature selection. The performance is evaluated through various measures including: Precision, Recall, F-measure, Accuracy, MCC and ROC. 12 Cleaned publically available NASA datasets are used for experiments. The results of both the dimensions of proposed framework are compared with the other widely used classification techniques such as: “Naïve Bayes (NB), Multi-Layer Perceptron (MLP). Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF)”. Results reflect that the proposed framework outperformed other classification techniques in some of the used datasets however class imbalance issue could not be fully resolved.