Along with the advancements in blockchain technology, many blockchain-based successful projects have been done mainly on the ethereum platform, most of which deal with transactions. Still, it also carries various risks when it comes to security, as evident from past attacks. Most big projects like uniswap, decentraland, and others use smart contracts, deployed on the ethereum platform, leading to similar projects via code reuse. Code reuse practice is quite frequent as a survey suggests 26% of contract code deployed is via code reuse. Smart contract code obfuscation techniques can be used on solidity code that is publicly verified, published (in the case of Ethereum), and on the deployment address. All the above techniques work by replacing characters with their random counterpart, known as statistical substitution. A statistical substitution is a process of transforming an input string into a new string where each character has been replaced by a random character drawn from a stock of all possible 'random' characters. Therefore, we proposed numerous methods in this paper to solve the above problems using various smart contract code obfuscation techniques. These techniques can be really useful in blockchain projects and can save millions of dollars to investors & companies by enhancing code security and preventing code reusability. Techniques mentioned in this paper when compared with other techniques. Our methods are not expensive to implement, very easy to use, and provide a developer-friendly selective increment in code complexity. 

We aim to extract emotional components within statements to identify the emotional state of the writer and assigning emoji related to the emotion. Emojis have become a staple part of everyday text-based communication. It is normal and common to construct an entire response with the sole use of emoji. It comes as no surprise, therefore, that effort is being put into the automatic prediction and selection of emoji appropriate for a text message. Major companies like Apple and Google have made immense strides in this, and have already deployed such systems into production (for example, the Google Gboard). The proposed work is focused on the problem of automatic emoji selection for a given text message using machine learning classification algorithms to categorize the tone of a message which is further segregated through n-gram into one of seven distinct categories. Based on the output of the classifier, select one of the more appropriate emoji from a predefined list using natural language processing (NLP) and sentimental analysis techniques. The corpus is extracted from Twitter. The result is a boring text message made lively after being annotated with appropriate text messages

Vehicle license number plate detection is essential for road safety and traffic management. Many existing systems have been proposed to achieve high detection precision without optimization of computer resources. Existing models have not preferred to use devices like smartphones or surveillance cameras because of high latency, data loss, bandwidth costs, and privacy. In this article, we propose a model of unloading decisions based on reinforcement learning (RL-OLD) for recognition and detection of vehicle license plates for high precision with optimization of computer resources. The proposed model detected different categories of vehicle registration plates by effectively utilizing edge computing. Our model can choose either the compute-intensive model of the cloud or the lightweight model of the local system based on the properties of the number plate. This approach has achieved high accuracy, limited data loss, and limited latency.

Developing nations like India have a huge potential for agricultural business and better cultivation. Because of the large size of cultivation land, improper water supply systems and lack of technology-based agricultural practices, there is a huge gap among expected and actual quantity and quality of agricultural products. Hence there is a need for significant revival in agribusiness using emerging technologies. The article proposes an intelligent water framework device called Agribot designed for the agricultural industry to minimize the water wastage and a better supply of cultivating materials using the Internet of Things (IoT). Our proposed IOT- Agribot will energize the water framework, improve the cost-effective water usage and reduce the labor force to achieve precision agriculture. The proposed IOT- Agribot has performed well for variable weather conditions, soli type, moisture content and crops.

Human presence detection is a continuously sought of an issue by the scientific community. Visual camera-based technologies have emerged recently with low cost and easy usage. However, these technologies have been increased the user privacy issues. Hence it is highly essential to design a human detection system without compromising the user privacy, comfort, cost and easy deployment. The pyroelectric infrared (PIR) based sensor systems are introduced however this technology is incapable to detect the presence of stationary human because it can detect the fluctuating signals only. In this paper, we have proposed a novel infrared (IR) based sensor system to detect the human presence either mobile or immobile in targeted locations with high accuracy. The proposed infrared (IR) sensor is designed to sense the heat radiation emitted by the human body, it detects the human presence accurately in targeted locations. The proposed IR based sensor system has successfully deployed in a targeted location and tested successfully for detecting the human presence and also other objects.