2692-4048ISSN (Online)
2770-0070ISSN (Print)
Passwords act as a first line of defense against any malicious or unauthorized access to one's personal information. With the increasing digitization, it has now become even more important to choose strong passwords. In this paper, the authors analyze a 100 million Email-Password Database to perform Exploratory Data Analysis. The analysis provides valuable insights on statistics about the most common passwords being used, character set of passwords, most common domains, average length, password strength, frequencies of letters, numbers, symbols (special characters), most common letter, most common number, most common symbol, the ratio of letters, numbers, symbols in passwords which highlights the general trend that users follow while creating passwords. Using the results of this paper, users can make intelligent decisions while creating passwords for themselves, i.e., not opting for the most common features that will help them create robust and less vulnerable passwords.
Read MoreDoi: https://doi.org/10.54216/FPA.040101
Vol. 4 Issue. 1 PP. 5-14, (2021)
One of the most significant parts of integrating computer technologies into intelligent transportation systems (ITS) is vehicle license plate recognition (VLPR). In most cases, however, to recognize a license plate successfully, the location of the license plate is to be determined first. Vehicle License Plate Recognition systems are used by law enforcement agencies, traffic management agencies, control agencies, and various government and non-government agencies. VLPR is used in various commercial applications, including electronic toll collecting, personal security, visitor management systems, parking management, and other corporate applications. As a result, calculating the correct positioning of a license plate from a vehicle image is an essential stage of a VLPR system, which substantially impacts the recognition rate and speed of the entire system. In the fields of intelligent transportation systems and image recognition, VLPR is a popular topic. In this research paper, we address the problem of license plate detection using a You Only Look Once (YOLO)-PyTorch deep learning architecture. In this research, we use YOLO version 5 to recognize a single class in an image dataset.
Read MoreDoi: https://doi.org/10.54216/FPA.040102
Vol. 4 Issue. 1 PP. 15-21, (2021)
In this paper, the authors analyze RFID technology, different types of Tags, Readers, and various protocols associated with RFID. We read, write and dump the raw bytes from the MIFARE Classic Card using RC5220 and Arduino Mega. Delhi Metro Card uses MIFARE DESFire. MIFARE DESFire uses 3(DES) for encryption. In this paper, we also read the data structure of the Delhi Metro Card. Additionally, we also compare MIFARE Classic Card and MIFARE DESFire Card.
Read MoreDoi: https://doi.org/10.54216/FPA.040103
Vol. 4 Issue. 1 PP. 22-31, (2021)
PROMETHEE II decision-making methodologies are integrated into a novel framework in this research. A real-world case study of lithium extraction techniques served as the basis for this investigation. Lithium extraction from brines and saltwater has become more difficult due to the limited natural resources of lithium and the worldwide desire to replace fossil fuels with clean and recyclable energy. Using a multicriteria decision-making approach, the suggested framework aids in selecting the best lithium extraction procedure from brines and saltwater. A case study of lithium extraction from brines and saltwater has been used. The findings of the study show that the suggested strategy is logical and enforceable.
Read MoreDoi: https://doi.org/10.54216/FPA.040104
Vol. 4 Issue. 1 PP. 32-40, (2021)
In recent years, spreading social media platforms and mobile devices led to more social data, advertisements, political opinions, and celebrity news proliferating fake news. Fake news can cause harm to networks, communications, and users and cause trust issues toward government, healthcare, or social media platforms. This inspired many researchers to implement models to detect falsified information content. But there are still many issues that need to be discussed and explored. In our paper, we introduce categories of fake news detection methods and compare these methods. After that, the promising applications for false news detection are extensively discussed in terms of fake account detection, bot detection, bullying detection, and security and privacy of social media. After all, A thorough discussion of the potential of machine learning approaches for fake news detection and interventions in social networks along with the state-of-the-art challenges, opportunities, and future search prospects. This article seeks to aid the readers and researchers in explaining the motive and role of the different machine learning fusion paradigms to offer them a comprehensive realization of unexplored issues related to false information and other scenarios of social networks.
Read MoreDoi: https://doi.org/10.54216/FPA.040105
Vol. 4 Issue. 1 PP. 41-57, (2021)