The use of a person's cell phone to commit fraud is known as cell phone fraud. Such scams are usually carried out through fake phone calls or text messages. The victim receives a call from a cell phone scammer, usually claiming to have an emergency or a legal problem. The purpose of the scam is usually to convince the victim to provide personal or financial information. This may include private information such as social security numbers, bank account details or credit card information. In addition, users are often subjected to unsolicited calls for marketing and information gathering initiatives such as campaigns, advertisements and surveys. In this study, a smartphone application built on the blockchain is created to stop these nuisance actions. Transaction times and performance tests have been rigorously performed according to the difficulty levels of the blockchain structure.

Mushroom consumption and wild mushroom gathering are increasing in our country and in the world. Mushroom poisoning has an important place in food poisoning cases. Mushroom poisoning accounts for approximately 7% of poisoning cases in adults. Mushroom collection and consumption is common in many regions of our country. In this study, a deep learning based mobile application was developed to reduce the incidence of mushroom poisoning by taking a photo of a mushroom and determining the type and toxicity of the mushroom from the photo. This mobile application is called MushAPP. In the first phase of the study, 5150 mushroom images of 20 mushroom species were used to create the dataset. The dataset was then pre-processed and converted into a format that can be used by the deep learning algorithm. The mobile application side of the project was developed in Android Studio IDE environment. An artificial intelligence model was integrated into the designed mobile application. In the application, the type and toxicity status of the mushroom viewed from the mobile device camera are determined and presented to the user. The research findings were analyzed and it was determined that the accuracy rate of the application in detecting the mushroom species was 99.8%.

When looking at the daily life flow and working sectors, it is seen that almost all work and transactions are carried out electronically. It performs many data streams in the electronic transactions performed. The importance of information security is exactly at this point. To ensure the security of the data, the journey of the data between the sender and the receiver is encrypted. In this study, a hybrid application that creates encrypted text using genetic algorithm and particle swarm algorithm has been developed. In the first step of the study, two separate keys were generated to encode the message using the genetic algorithm and particle swarm algorithm. Shannon Entropy method was used as a fitness function in both algorithms. The message was encrypted with the genetic algorithm method by choosing the key that obtained the best result from the compliance function. The encrypted message was decoded by applying a reverse genetic algorithm to the recipient. The encryptions made using the generated key were measured and the results of the AES algorithm were compared. In the proposed model, successful performances were obtained as the maximum switching space and encryption time for encryption. As a result, the proposed application offers an alternative method of data encryption and decryption that can be used for message transmission.

In this study, a cloud-based android application and centralized tracking software were developed to perform an accurate and uninterrupted tree count across open lands. The application is used to count the desired number of trees and species at the same time. User-logged data and location information are saved in real-time to the application's cloud database. The application can work online and with offline mod. In cases where there is no internet connection, it inserts the data to the local SQLite database. After the connection is established, the pairing continues. It's used Google Firebase on the cloud server for data storage. The processing of target locations and GPS coordinates was developed with the Google Map Library. The tree counting application automatically picks up the user's current location when it is first opened. The counting starts after the tree and tree species that the user has selected from the menu. The software developed shows that tree counting is done simultaneously at the desired point. It also solves confusion caused by different tree species during the counting. We've received feedback from 100 people using the application. The users answered five questions. As a result, it is aimed to provide a comfortable transition between tree species and its users with its simple use to eliminate the complexity of counting and save time.