CC BY
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o Example: SpamAssassin, which integrates heuristics and statistical techniques. Challenges in SMS Spam Filtering
1. Dynamic Nature of Spam:
Spammers constantly evolve their tactics, requiring adaptive filtering mechanisms.
2. Privacy Concerns:
Filtering systems must ensure user privacy, avoiding excessive data collection or intrusive analysis.
3. Resource Constraints:
Real-time filtering on mobile devices demands efficient algorithms with minimal computational overhead.
4. Language and Regional Variations:
SMS spam often uses multiple languages and local slang, complicating detection efforts. Evaluation Metrics
Effectiveness of SMS spam filtering systems is typically evaluated using metrics such as:
• Precision: Proportion of correctly identified spam messages.
• Recall: Ability to identify all spam messages.
• F1 Score: Harmonic mean of precision and recall, offering a balanced evaluation. Case Studies
1. Spam Filtering in Commercial Services:
Google Messages and Apple's SMS filters use AI to automatically detect spam.
2. Open Source Solutions:
Projects like SpamAssassin demonstrate the utility of hybrid approaches for spam filtering. Effective SMS spam filtering is crucial for maintaining trust and efficiency in digital communication. While traditional techniques offer a foundational approach, advanced methods such as machine learning and hybrid systems provide the adaptability needed to counter evolving spam tactics. Future innovations, including federated learning and blockchain, promise to enhance both accuracy and user privacy, paving the way for more robust spam mitigation strategies.
Список использованной литературы:
1. Sebastiani, F. (2002). Machine Learning in Automated Text Categorization. ACM Computing Surveys.
2. Almeida, T. A., Hidalgo, J. M. G., & Yamakami, A. (2011). SMS Spam Collection. Proceedings of the ACM Symposium on Applied Computing.
3. Sumeetha, T., & Younus, S. S. (2016). Efficient SMS Spam Detection Using Machine Learning Algorithms. IEEE Transactions.
© Yazmedov H., 2024
УДК 62
Yoldashova A., student. Esenova E., teacher.
Oguz han Engineering and Technology university of Turkmenistan.
Ashgabat, Turkmenistan.
DESIGN AND IMPLEMENTATION OF A SUPERMARKET MANAGEMENT SYSTEM (GULZEMIN)
Annotation
This paper explores the design and implementation of a supermarket management system tailored for
"Gulzemin," a retail chain in Turkmenistan. The system aims to digitize inventory tracking, sales, and customer management while addressing the specific needs of the local retail market. By integrating modern technologies, the system enhances operational efficiency, reduces errors, and improves customer experience. Key challenges such as language localization, regulatory compliance, and user training are also addressed. The study concludes with recommendations for future developments to further enhance system functionality.
Keywords:
supermarket management system, Gulzemin, retail software, inventory management, sales tracking, digital
retail solutions, Turkmenistan retail technology.
In today's competitive retail environment, supermarkets must adopt advanced management systems to streamline operations, reduce costs, and enhance customer satisfaction. This is particularly relevant in Turkmenistan, where the retail market is rapidly evolving. "Gulzemin," a leading supermarket chain, requires a comprehensive digital solution to manage its growing operations effectively.
This paper presents the design and implementation of a supermarket management system tailored for Gulzemin. The system focuses on core functionalities such as inventory management, sales tracking, employee scheduling, and customer loyalty programs.
Objectives of the Gulzemin Supermarket Management System
1. Streamlined Operations:
o Automate routine tasks such as inventory updates and sales tracking.
2. Enhanced Customer Experience:
o Offer faster checkout processes and personalized promotions.
3. Localization:
o Incorporate Turkmen language and regional business practices into the system.
4. Regulatory Compliance:
o Ensure adherence to local tax and business regulations.
System Features
1. Inventory Management:
o Real-time tracking of stock levels, expiration dates, and reorder alerts.
2. Point of Sale (POS):
o Seamless integration with barcode scanners, receipt printers, and payment systems.
3. Employee Management:
o Shift scheduling, attendance tracking, and payroll processing.
4. Customer Relationship Management (CRM):
o Loyalty programs, personalized discounts, and feedback mechanisms. System Design and Architecture
1. Front-End Interface:
o User-friendly interfaces for staff and customers, developed using React.js.
2. Back-End Framework:
o Robust server-side management using Python Django.
3. Database:
o PostgreSQL for secure and scalable data storage.
4. Cloud Integration:
o Cloud-hosted services using AWS for data backup and high availability. Implementation Process 1. Requirements Gathering:
o Interviews with Gulzemin staff and analysis of existing workflows.
2. System Design:
o Prototyping to visualize the system and gather user feedback.
3. Development:
o Coding modules for inventory, sales, and employee management.
4. Testing:
o Conducting unit and system tests to ensure reliability and performance. Challenges and Solutions
1. Language Localization:
o Challenge: Adapting the interface for Turkmen speakers. o Solution: Implementing a bilingual interface in Turkmen and English.
2. User Adoption:
o Challenge: Resistance to new technology among staff. o Solution: Comprehensive training and user-friendly design.
3. System Scalability:
o Challenge: Accommodating Gulzemin's expansion. o Solution: Modular system design for easy scalability.
The Gulzemin supermarket management system represents a significant advancement in retail operations in Turkmenistan. By integrating modern technology, it enhances efficiency, customer satisfaction, and data-driven decision-making. While challenges such as user adoption and scalability remain, the system's modular and adaptable design positions it as a future-ready solution for the retail sector. References:
1. Laudon, K. C., & Laudon, J. P. (2020). Management Information Systems: Managing the Digital Firm. Pearson.
2. Stair, R., & Reynolds, G. (2019). Principles of Information Systems. Cengage Learning.
© Yoldashova A., Esenova E., 2024
УДК 62
Yusupov Yh., student.
Myradov R., teacher.
Oguz han Engineering and Technology university of Turkmenistan.
Ashgabat, Turkmenistan.
ONLINE DOCTOR CONSULTATIONS AND TELEMEDICINE SERVICES
Annotation
The advent of telemedicine has transformed healthcare delivery by offering remote consultations, diagnosis, and treatment through digital platforms. This paper examines the impact of online doctor consultations and telemedicine services on healthcare accessibility, patient outcomes, and cost efficiency. Key technologies enabling telemedicine, such as video conferencing, electronic health records (EHRs), and artificial intelligence (AI), are discussed alongside challenges such as data privacy, digital literacy, and regulatory compliance. The paper concludes with future directions for telemedicine in a post-pandemic world.
Key words:
telemedicine, online doctor consultations, digital healthcare, electronic health records, virtual care, healthcare
accessibility, data privacy, AI in healthcare.
