The article is devoted to a brief review of approaches to the analysis of social relations in social networks using comments and credentials located in the profiles of social network users. The study aims to determine the interest and behavior of each user. The approach that we propose to determine the interests of social network users requires some methods of machine learning (classification analysis and data clustering). A method based on sentiment analysis and a naive Bayesian classifier is proposed. Determining the interests of social network users based on the intellectual analysis of comments can help to understand the logic of their behavior, and determine social relations between users and problems in society.

The economics of a nation is significantly influenced by agricultural productivity. Finding plant leaf disease is crucial since it significantly reduces agricultural productivity. Traditional detection methods like observing with the naked eye can lead to time-consuming and less accurate results. Farmers can’t always tell the difference between leaf diseases because sometimes they look the same. That’s why researchers have started using automation techniques to accurately detect the main diseases and their symptoms. This research proposed potato leaf disease detection using an image processing technique where the dataset was obtained online. In the proposed method, several image pre-processing techniques are used including data augmentation, gaussian smoothing, image normalization, dimensionality reduction and one hot encoding. CNN, KNN and SVC were used as classifiers. CNN gives the best result with an overall accuracy of 97%. Previous works with different classifiers had several limitations and using CNN the researchers didn’t get satisfying result. For this research a new hybrid model is introduced which can utilize the best of CNN classifier and it will be much more reliable and effective.

Massive Open Online Courses (MOOC) platforms play a vital role nowadays in knowledge creation and enhancement. Though, initially these programs and courses were offered freely, now due to emergence of technology and requirement, they were offered in both free and paid mode. Currently, the MOOCs provide various programs and courses that are administered by various international higher education institutions and private organizations. Moreover, the COVID – 19 has triggered all the universities, colleges and institutions to move towards MOOC platforms as an online learning method. The students used these MOOC platforms to gain and enhance required knowledge in this competitive world. Due to this emergence, various research studies were conducted during the past years using different research methodologies and scopes to identify the usage, gaps, trends, advantages, disadvantages and user satisfactions. This paper reviews some major researches since 2010 and summarizes the findings to support future research scopes. The 16 research articles that were having more than 100 Google citation counts were considered for this study and analyzed. Currently, the number of students joining and completing the courses through online is significantly increased which showed that the learners’ intention on getting the knowledge in the area of specialization has increased. Moreover, the acceptance and recognition of online courses and programs by various employers and institutions have been increase which opens a great opportunities for continuous and life-long learnings. Though, the completion rates of courses and programs thorough online platforms are increased comparatively now-a-days, the success rates, knowledge acquisition rates, actual participation rates and learning outcome attainment rates need to be studied in depth.

In the past few years, the number of IoT devices has grown substantially, and this trend is likely to continue. An increasing amount of effort is being put into developing software for the ever-increasing IoT devices. Every IoT system at its core has software that enables the devices to function efficiently. But security has always been a concern in this age of information and technology. Security for IoT devices is now a top priority due to the growing number of threats. This study introduces best practices for ensuring security in the IoT, with an emphasis on guidelines to be utilized in software development for IoT devices. The objective of the study is to raise awareness of the potential threats and emphasize the use of secure software development lifecycle. The study will also serve as a point of reference for future developments and provide a solid foundation for securing IoT software and dealing with vulnerabilities. This paper divides the most common concerns with Internet of Things (IoT) security into three primary categories, and then further subdivides each of those categories into other subcategories.

When information is set for easy retrieval, access, updation, and management, then it is known as Database. Its major purpose is storing aggregations of records and data files that contain information regarding transactions, customer details, product details and financials. The paper deals with a briefing on DBMS which is a collection of inter-related data and set of programs to retrieve and access the data which focuses on :- a) exchange of data, b) retrieve the data, c) storage of data, and d) manipulation on data. The paper also gives a glance on elements of DBMS also describes the architecture which is consist of: a) internal level, b) conceptual level, and c) external level. The paper also mentions some of the uses of databases and will try to give an idea about the applications of Database in different MNC’s and projects in Data ware.