Since the beginning of the 2010s major car manufacturers progressively started to invest in Autonomous Vehicles(AV). Notable examples are Tesla, Ford, and Toyota. Small startups and tech giants like Google and Yandex are now also entering the competition to propose new solutions. Most of them have already developed their prototypes of driverless vehicles that you can normally observe in the streets of New-York, USA, or Innopolis, Russia. Despite the significant hype on this technology, current solutions are not always optimal and multiple challenges are left open in a domain such as security, data integrity, privacy, and communication. Blockchain is one of the most appealing technologies to be used in this domain since it provides solutions for some of these challenges. In this paper, we describe, categorize, and evaluate different solutions of the Autonomous Vehicles Industry that makes use of Blockchain. We use a software engineering approach to organize the existing work in multiple categories such as challenges addressed, quality attributes promoted. This work is intended to provide researchers in the field with a well-defined and structured categorization, plus insights into the existing literature.

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Telegram chatbots are one of the main sections in Telegram applications that get more attention and provide huge business opportunities for companies nowadays. Also, Machine learning algorithms have been used in numerous fields and industries and one of the main growing markets is real estate. It is already helping real estate agents to respond more quickly to clients’ questions and develop landing pages that fit customers’ needs. However, one of the main time-consuming stages for a real estate agent is the process of answering questions about price per location and knowing which locations clients are interested in. In order to solve these problems, we have built a telegram chatbot to answer customers of any real estate agent with the price of a real estate property based on their current Geolocation. This bot can predict the price of a real estate property using room numbers, Geolocation, and surface area in square meters. These are the inputs for machine learning algorithms to give an approximate price. The bot can predict based on the source of the data-set, for example, we have collected our data from classified ads website for real estate in Amman, Jordan. We have used python and web scraping library for data extraction, cleaning, and transformation. As a result, by using Geolocation we have increased the model accuracy to 1.3X and our bot can be replicated in any markets based on the dataset. We believe our work can be taken to different markets and make real estate agent’s job easy and more profitable by changing leads to potential customers thought answering their questions.

The energy industry needs to shift to a new paradigm from its classical model of energy generation, distribution, and management. This shift is necessary to handle digitization, increased renewable energy generation, and to achieve goals of environmental sustainability. This shift has several challenges on its way and has been seen through research and development that blockchain which is one of the budding technology in this era could be suitable for addressing those challenges. This paper is aimed at the survey of all the research and development related to blockchain in the energy industry and uses a software engineering approach to categories all the existing work in several clusters such as challenges addressed, quality attribute promoted, the maturity level of the solutions, etc. This survey provides researchers in this field a well-defined categorization and insight into the existing work in this field from 3 different perspectives (challenges, quality attributes, maturity).

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