Software called Inventory Management System is useful for companies who run hardware stores, wherethe store owner maintains track of sales and purchases. Poor inventory management results in dissatisfied customers, too much money being held in warehouses, and slower sales. The paper work is eliminated with this initiative, human error, manual delay, and process acceleration. A store owner can use an inventory management system to keep track of sales and available stock, as well as to determine when and how much to reorder. A Windows application called Inventory Management System was created for Windows operating systems with a focuson inventory control and the ability to produce all the necessary reports.

The E-voting system offers internet voting. To stop voter fraud, we use two different levels of security. As the first level of security, a web camera records the voter's face and uploads it to the database. After that, the person's face is compared to the face in the database to verify its authenticity. The two faces are compared with the help of the Local Binary Pattern algorithm. The plan's foundation is combining two images and assigning a value to a central pixel. Those central pixels will have values of either 0 or 1. If the value is less than a pixel, a histogram of the labels is calculated and used as a descriptor.

Inventory Management System software is important for organisations that operate hardware stores, where the store owner keeps track of sales and purchases. Poor inventory management leads to disgruntled customers, excess money in warehouses, and slower sales. With this initiative, paper work, human mistake, manual delay, and process acceleration are all avoided. An inventory management system can help a store owner keep track of sales and available goods, as well as calculate when and how much to repurchase. Inventory Management System is a Windows application for Windows operating systems that focuses on inventory control and the ability to generate all essential reports.

Many researchers have put their efforts into defence against this Advanced Persistence Threat (APT) attack. The traditional security systems such as web and email protectors and canners are no longer suitable for defending and preventing damages. The proposed system helps to detect ATP attacks from the network traffic data using a convolution Neural network (CNN). The experimentation is performed on the NSL-KDD dataset. Feature engineering is a major part of the system where we can select the 14 most appropriate features among 42 available features. One of the most effective approaches to APT attack detection is to use machine learning to analyse network traffic. The proposed method gives superior results in the detection of APT attacks.