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From few decades AI almost uses in everywhere to make a decision, in problem solving concept etc. As AI stand for Artificial Intelligence a human generated intelligence of machine which is used to solve many problems either by self learning or by human interaction. AI uses to solve complex problem using intelligences by Reasoning learning, and self-correction. Machine learning and deep learning are component of AI, ML is a algorithmic approach while Deep learning use multi layer neural networks for data analysis. Deep learning is a subset of machine learning in which huge amount of data are used to learn from multilayered neural network. Machine learning is an algorithmic approach where the performance of algorithm is increased as the amount of data increased. Machine learning belongs to the artificial intelligence as a subset. Artificial intelligence is a set of command that designed to sense, act and adapt by self learning. It is learns like a human, as human learn from their experiences, in artificial intelligence machine or algorithm learn by the earlier database. As like the human learn more as he/she became more experience similarly in this area machine and algorithm become more accurate and useful as the amount of data increases. Nowadays, deep learning used as a tool in various study areas such as speech theory, computer vision, NLP, health sector and many more [1][2]. [ABSTRACT FROM AUTHOR]

The problem of obtaining an optimal scheduling of dependent tasks in homogeneous multiprocessor system is well known as an NP-hard problem. Heuristic is the best way to solve this problem. In a homogeneous multiprocessor system, task execution time is independent of the machine to which it is assigned. Recent research in scheduling has shown that list scheduling algorithms usually obtain more efficient and less complex schedules than other known algorithms. In this paper, some basic list scheduling algorithms, namely, LPT, SPT, ECT and EST are compared on the basis of performance parameter Makespan in two different environments. In the first environment, all the processes arrive at 'zero' time instantly, while in the second environment, all the processes arrive randomly. Simulation results show that the makespan of LPT is better than other algorithms in both environments. [ABSTRACT FROM AUTHOR]