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Фитратович В. и др. МАТЕМАТИЧЕСКАЯ МОДЕЛЬ МНОГОФАЗНОЙ
ФИЛЬТРАЦИИ В НЕФТЕГАЗОВОМ ПЛАСТЕ ПРИ ЕГО ЗАВОДНЕНИИ
//INTERNATIONAL CONFERENCES ON LEARNING AND TEACHING. – 2022.
– Т. 1. – №. 4. – С. 520-525.
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Jamshid S. ENTROPY EVALUATION CRITERION IN DECISION TREE
ALGORITHM EVALUATION //International Journal of Contemporary Scientific and
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FINGER PRINT-BASED ATTENDANCE SYSTEM
Sherbaev Javokhir Ravshan ugli,
Abdurakhmanov Ravshan Anarbayevich
Jizzakh branch of National University of Uzbekistan
Annotation:
The Fingerprint-Based Attendance System has emerged as a robust
and secure method for accurately recording attendance in various organizations and
educational institutions. This research paper explores the development,
implementation, and evaluation of such a system, highlighting its advantages,
challenges, and potential future enhancements. Through a combination of literature
review and practical experimentation, this paper aims to provide insights into the
effectiveness and reliability of fingerprint-based attendance systems.
Keywords:
Fingerprint, Attendance Management, Authentication.
Attendance tracking is a crucial aspect of organizational management and
educational institutions. Traditional methods of taking attendance, such as manual
paper-based systems or card swiping, have proven to be inefficient and susceptible to
fraud. In contrast, fingerprint-based attendance systems offer a more secure, accurate,
and convenient solution.
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Fingerprint verification is integrated into the mechanism of attendance control
for students with an electronic attendance management scheme. Enrolment and
verification are the two stages that make up this system.
A person’s biometric details are collected during enrolment, and the personal
data is extracted and recorded in a database with a personal ID. After feature extraction,
the enrolment module aims to accept a person with an ID number and fingerprints into
a database.
These characteristics form a blueprint for determining the user's identity and
formulating the authentication mechanism. A supervisor of the attendance control
scheme does the enrolment process. During verification, the user's biometrics are
collected once again, and the collected features are compared to those already stored
in the database to see if they fit. Following a good match, attendance is recorded using
the user's id that was used to match the models.
Fingerprint attendance systems have gained popularity as a reliable method for
tracking employee or student attendance. They offer several advantages and
disadvantages:
Accuracy: Fingerprint recognition is highly accurate, minimizing the chances of
false entries or "buddy punching" (when someone else clocks in for another person).
Security: Fingerprint patterns are unique to each individual, making it difficult
for unauthorized personnel to gain access or manipulate the attendance system.
Convenience: Employees or students don't need to carry cards or remember
PINs, reducing the likelihood of forgotten or lost credentials.
Speed: The process of clocking in and out is quick, saving time for both
employees and employers.
Data Integrity: Fingerprint data is difficult to tamper with, ensuring the integrity
of attendance records.
Reduced Costs: Over time, fingerprint attendance systems can save money
compared to traditional methods that require physical cards or badges. Ease of
Integration: Many fingerprint attendance systems can be integrated with other
software, such as payroll or HR management systems, streamlining administrative
processes.
Remote Access: Some systems allow for remote monitoring and management,
which can be helpful for companies with multiple locations or organizations with off-
site employees.
Cost-effective: RFID systems can be relatively inexpensive to set up,
particularly if using passive tags. Over time, the reduced labor costs and increased
efficiency can lead to a significant return on investment.
Scalability: RFID technology can be easily scaled up or down to meet the needs
of different organizations. This makes it an ideal solution for businesses and
institutions of various sizes
Cost: Implementing a fingerprint attendance system can be expensive, including
the initial hardware and software costs.
Privacy Concerns: Some individuals may have concerns about the storage and
security of their fingerprint data, fearing it could be misused or compromised.
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Maintenance: Fingerprint scanners require regular maintenance to ensure accurate
readings. Dust, dirt, or scratches on the scanner can affect performance.
Hygiene: In shared environments, concerns about hygiene may arise, as many
people may have to touch the same fingerprint scanner throughout the day.
Technical Issues: Like any electronic system, fingerprint attendance systems can
experience technical glitches, such as software crashes or hardware failures.
Enrollment Process: Enrolling fingerprints for a large number of users can be
time-consuming, especially if there are errors or difficulties with certain individuals'
prints.
Environmental Factors: Extreme temperatures, humidity, or dirty environments
can affect the performance of fingerprint scanners.
User Resistance: Some individuals may be uncomfortable with the idea of
providing their fingerprints for attendance tracking, leading to resistance or reluctance.
References:
1.
K.Jaikumar1 , M.Santhosh Kumar2 , S.Rajkumar3 , A.Sakthivel4 fingerprint
based student attendance system with sms alert to parents
2.
B. Rasagna, Prof. C. Rajendra “SSCM: A Smart Systemfor College
Maintenance” International Journal of Advanced Research in Computer Engineering
& Technology, May 2012.
3.
S. Gong, S.J. McKenna, and A. Psarrou, Dynamic Vision: from Images to
Face Recognition, Imperial College Press and World Scientific Publishing, 2000.
4.
Kai-Fu Lee, Hsiao-Wuen Hon, and Raj Reddy, An Overview of the SPHINX
Speech Recognition System. IEEE Transactions on Acoustics, Speech and Signal
Processing.
ENHANCING NETWORK SECURITY THROUGH AI-DRIVEN
SOLUTIONS
Akhunbayev Adil Alimovich,
Khusanboyev Mukhammadbobir Alisherjon ugli,
Isroilov Ikhtiyorjon Ikromjon ugli
Fergana Polytechnic Institute, Uzbekistan
Annotation
: The rapid evolution of cyber threats demands innovative
approaches to network security. This article delves into the realm of AI-driven network
security, exploring how artificial intelligence is revolutionizing threat detection,
response, and prevention in modern network infrastructures. We discuss the key
techniques, benefits, and challenges associated with AI in network security.
Keywords
: Network Security, Artificial Intelligence, AI-Driven Security,
Threat Detection, Behavioral Analysis, Machine Learning Models, Deep Learning,
Natural Language Processing (NLP), Automated Response, Anomaly Detection,
Cybersecurity, Privacy-Preserving AI, Adversarial Attacks, Scalability, Real-Time
