Microbial Fuel Cell (MFC) is a bio-electrochemical device that generates electric current by using bacteria. MFCs are currently a topic of intense research and interest due to their ability to produce renewable energy along with added benefits such as wastewater treatment. Although the theoretical concepts and applicability of MFCs are great, their application, thus far has been limited due to the limits of power production. Current research aims to improve the efficiency as well as the upper limit of power production by MFCs. In parallel to current research, this study is designed with a similar aim to do a comprehensive data analysis on the topic of MFCs by using techniques of Artificial Intelligence. Therefore, we started this study by obtaining the relevant data through an extensive literature retrieval for developing Artificial Neural Network model. The data from the output layer was viewed by using VOSviewer software and was further subjected to analysis. The data collected through machine learning provided an insight about the optimal conditions of MFCs which would allow for maximum current production. It discusses two existing types of MFCs; namely mediator type and mediator free type of MFC. Anode respiring bacteria (ARB), also known as exoelectrogenes can be used as the mediator to transfer electrons by utilizing the substrate present at the anode. Our results suggest that different combinations of bacterium and biofilms can produce more electric current with improved stability. This study will provide an insight to improve the working capacity of MFCs. It is likely that MFCs will one day be used as a stand-alone power production method by optimizing the current production capacity. Moreover, these advancements will have a significant by utilizing MFCs for making chips and biosensors, and treating wastewater.

Cyber security is one of the fundamental research areas of software engineering. The systems that make up today's information systems infrastructure have been developed largely with software support. Security vulnerabilities in the software used in these systems may cause undesirable results. It is very important to manage software vulnerabilities correctly. In addition, an effective communication mechanism and certain standards should be established among those working in this field. The importance of the subject has been understood in recent years and the studies in this area have gradually increased. The use of machine learning algorithms is increasing in recent studies in this area. Although there is a large data set accumulated in vulnerability databases, there is often the problem of unstructured data. Vulnerability databases and security reports are created in natural language that people can understand and interpret. These reports are difficult to read and understand by machines. Our study focuses on the difficulties of this unstructured and natural language system. In order to investigate this problem, firstly, up-to-date and accessible databases used in scientific research were examined and evaluated. Then, a three-stage security framework was proposed, consisting of the use of vulnerabilities by machines to assist experts from the notification stage to the reporting stage. The rules and flow charts of each stage are defined. In order to increase the usability of different databases in their own systems, the framework rules are defined as a guideline containing flexible directions, not rigid items. The point of consideration is not the methods and tools used, but the definition of outputs as common and similar attributes.

A plethora of courier services are available in Bangladesh which are more expensive, less effective and takes ages to deliver the product. Besides, in the ongoing COVID-19 pandemic situation, it’s onerous to move from one place to another, and if a person wants to send a product, he/she needs to go to the courier office. Moreover, many people need to move from their house for attending office, meetings, and traveling from one district to another district for special purposes. In this paperwork, a system is introduced to reduce the cost and time to send a product from one place to another place. Basically, an application is developed to meet the people who want to send a product and another one who want to carry the product. For carrying the product one can easily earn money. After scrutinizing the cost of courier services in Bangladesh, an algorithm is introduced to calculate the carrying cost of the product and an algorithm is also developed for product security.

Performance is a point of interest that is quite interesting for application owners, how could it not, besides being rich in features and following what they want, there are things that are no less important, namely the issue of the speed of use of an application that must be considered for developers. Because the speed level will affect the user experience in using it. Many factors influence the performance of an application, especially in the website category, one of which is the developer's ability to minimize large amounts of data load. This is the importance of being able to categorize which large data loads need to be considered and which are not. There is a system crowdfunding website that is currently operating using the laravel framework, but there are several obstacles faced where some of the processes in it are rather slow in the process. In this research, how do we choose which process uses process eloquence and which one needs to use the query builder. So that combining eloquent and query builder can be optimal

Gross Domestic Product is one of the most important economic indicators of the country and its positive or negative growth indicates the economic development of the country. It is calculated quarterly and yearly at the end of the financial year. The GDP growth of India has seen fluctuations from last few decades after independence and reached as high as 10.25 in 2010 and declined to low of -5.23 in 1979. The GDP growth has witnessed a continuous decline in the past five years, taking it from 8.15 in 2015 to 1.87 in 2020.The lockdown imposed in the country to curb the spread of COVID-19 has caused massive slowdown in the economy of the country by affecting all major contributing sectors of the GDP except agricultural sector. To keep on track on the GDP growth is one of the parameters for deciding the economic policies of the country. In this study, we are analyzing and forecasting the GDP growth using the time series forecasting techniques Prophet and Arima model. This model can assist policy makers in framing policies or making decisions.