In today’s world, many artificial intelligence applications developed using programming languages like Python, R and so on. Each language comes with its own programming structure and syntactical forms. Programmers are broadly classified into three categories namely, novice users, knowledge intermittent and expert one. For novice users, it is always a challenge to write a code without typographic errors though users know theoretical knowledge of Programming language, its structure and syntax as well as logic of program. Therefore, this paper explores use of voice recognition technique in the field of programming, specifically for writing program with Python programming language. In experimental analysis, it found helpful for new Python programmers and provide new learning curve for programmers wherein beginner can experience hassle free program writing. This paper adds new way of creating interest in beginners for judging their coding paradigm understanding and explore one of the area for user experience field for better programming Integrated Development Environment Development (IDE).

The internal combustion engine-based transportation system is causing severe problems such as rising levels of pollution, rising petroleum prices, and the depletion of natural resources. To divide power between the engine and the battery in an effective manner, a sophisticated energy management system is required to be put into place. A power split strategy that is efficient may result in higher fuel economy and performance of Electric Vehicles (EVs). In this paper, we propose the reinforcement learning method using Deep Q learning (DQL), which is a novel Improved Swarm optimized Deep Reinforcement Learning Algorithm (IS-DRLA) designed for energy management control. To perform an update on the weights of the neural network, this method computes the use of a modified version of the swarm optimization technique. After that, the suggested IS-DRLA system goes through training and verification using high-precision realistic driving conditions, after which it is contrasted with the standard approach. The performance indices such as State of Charge (SOC) and fuel consumption and loss function are analyzed for the efficiency of the proposed method (IS-DRLA). According to the findings, the newly proposed IS-DRLA is capable of achieving a higher training pace with a lower overall fuel consumption than the conventional policy, and its fuel economy comes very close to matching that of the worldwide optimal. In addition to this, the adaptability of the suggested strategy is demonstrated by utilizing a different driving schedule.