The objective of this study is to explore the role of artificial intelligence applications (AIA) in education. AI applications provide the solution in many ways to the exponential rise of modern-day challenges, which create difficulties in access to education and learning. They play a significant role in forming social robots (SR), smart learning (SL), and intelligent tutoring systems (ITS) to name a few. The review indicates that the education sector should also embrace the modern methods of teaching and the necessary technology. Looking into the flow, the education sector organizations need to adopt AI technologies as a necessity of the day and education. The study needs to be tested statistically for better understanding and to make the findings more generalized in the future.

The aim of the article is to explore the academic and administrative applications of Artificial Intelligence. Teachers have the main responsibility of teaching in any educational setting. But there are various other tasks to be performed by the teachers as well. Besides academic duty, most of the teacher’s time and educational resources are dedicated to administrative works. Artificial Intelligence Applications (AIA) are not only assisting education academically and administratively but also enhance their effectiveness. AIA provides help to teachers in various types of tasks in the shape of Learning Analytics (LA), Virtual Reality (VR), Grading/Assessments (G/A), and Admissions. It minimizes the administrative tasks of a teacher to invest more in teaching and guiding students. In the current era, where there are a lot of tasks associated with the teaching profession, AIA adds a significant contribution to enhance student learning, minimize the workload of a teacher, grade/assess the students effectively and easily, and to help in a lot of other administrative tasks. The study needs to be quantitatively checked to make it generalized and acceptable.

arallel architectures have clearly emerged as the future environments for high-performance computation for most applications. The barrier to their widespread use is that writing parallel programs that are both efficient and portable is quite difficult. Parallel programming is more difficult than sequential programming because parallel programs must express not only the sequential computations, but also the interactions (communication and synchronization) among those computations that define the parallelism. T o achieve good performance, programmers must understand this large-scale structure.Most current text-based parallel-programming languages either implicitly define parallel structure, thus requiring advanced compilation techniques, or embed communication and synchronization with sequential computation, thus making program structure difficult to understand. Furthermore, direct use of vendor-supplied procedural primitives can preclude portability. We could alleviate these difficulties with a programming process that separates specification of sequential computations from specification of synchronization and communication, and expresses synchronization and communication directly but abstractly.Directed-graph program representations can separate these specifications by permitting a two-step programming process in which programmers first design sequential components and then compose them into a parallel structure. This provides a simplified divide-and-conquer approach to design.Such program representations have other advantages. They directly represent multiple threads of control. They also display and expose the large-scale program structure that parallel programmers must understand Spring 19951063-6552/95/$4.00 0 1995 IEEE

In this paper, Pakistan's energy security performance based on a study of its energy security index during 1991-2018 is assessed. The assessment, based on an analysis of the energy security and energy policies of Pakistan, focuses on the concepts of "Availability", "Affordability", "Technology", "Governance", and "Environment". Twenty-two indicators, termed the Energy Security Indicators for Pakistan (ESIP), were selected. The ESIP were normalised using the zscore method and weighted based on principal component analysis (PCA). The index analysis revealed that Pakistan's energy security performance decreased between 1991 and 1999, followed by an increase until 2018. A maximum score of 8.36 was attained in 1991, and a minimum of 7.59 was achieved in 1999. During 2000-2018, the performance improved, resulting in a score of 8.29 in 2018. Supply, consumption, and import indicators played a key role in the energy security performance in the studied period.

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