Database Administrators Research Articles

The Development of Operational Model Integrating Digital Technologies into Public Mental Healthcare Services in Nigeria

The availability and utilization of digital technologies can leverage the challenges of the sharp-depleting workforce due to the consistent migration of healthcare professionals (HCPs), the alarming ratio of the number of patients to HCPs, discretional use of digital technologies among HCPs in providing effective therapeutic interventions and mental healthcare services. This study aimed to develop an operational model that integrates digital technologies in mental healthcare service delivery for public facilities in Nigeria. The practice-oriented theory guides the development of the model. A qualitative method assessed the digital technologies used during the COVID-19 pandemic. A non-probability, purposive sampling technique that employed the snowball method was used in selecting the sixteen (16) HCPs from four (4) neuropsychiatric hospitals in South-West Nigeria. Thematic analysis was conducted on qualitative data. Findings from the qualitative analysis revealed several challenges in providing adequate and efficient services in public mental healthcare facilities in Nigeria, such as human resource challenges, brain drain, lack of infrastructure, and strains on HCPs. Findings from this study indicated that digital technologies are sparingly utilized in public mental healthcare service delivery due to a lack of policy and framework. HCPs perceive digital technologies to be beneficial to themselves and their clients. The highlighted benefits to patients are reduced stigmatization levels, decreased financial burden, enhanced accessibility to therapists, strengthened adherence, mitigated relapse incidences and improved intervention. The transition from the traditional/conventional space of mental healthcare service delivery to digital space requires a collaborative approach with Computer scientists (software developers, database administrators), Data scientists, investors, stakeholders and mental HCPs. The proposed model serves as a contemporary framework that the Government, policymakers in the Ministry of Health and management of mental health facilities in Nigeria can adopt, providing the needed awareness of the benefit of technological intervention.

Developing A Tourism Information Portal Using Web Technologies and Database Management

The quality and variety of the infrastructure at a place has a big impact on tourism. An essential component of the tourism sector, infrastructure has a unique influence on the growth of this rapidly developing sector. Many nations have realised how important infrastructure is to the travel and tourist sector, and as a result, their governments have partnered with the sector to provide infrastructure that is specifically needed for travel. The sites that draw tourists in the first place, where tourism is delivered, where businesses are located, and where tourists consume the product are all considered destinations. The design and implementation of a tourism information portal using web technologies and database administration is presented in this study. The portal's goal is to give travellers a thorough and intuitive way to get information about travel destinations, lodging options, and activities. Database design, web application development, and the integration of pertinent technologies like HTML, CSS, JavaScript, and PHP are all part of the development process. User testing and feedback are used to assess the portal's usability and functionality. The outcomes show how successful the portal is at delivering precise and current information, improving the traveler experience, and encouraging destination management. The study emphasises the use of web technologies and database management in tourism marketing and management, and it advances the development of tourism information systems.

Harnessing probabilistic neural network with triple tree seed algorithm-based smart enterprise quantitative risk management framework

Enterprise risk management (ERM) frameworks convey vital principles that help create a consistent risk management culture, irrespective of employee turnover or industry standards. Enterprise Management System (EMS) are becoming a popular research area for assuring a company’s long-term success. Statistical pattern recognition, federated learning, database administration, visualization technology, and social networking are all used in this field, which includes artificial intelligence (AI), data science, and statistics. Risk assessment in EMS is critical for enterprise decision-making to be effective. Recent advancements in AI, machine learning (ML), and deep learning (DL) concepts have enabled the development of effective risk assessment models for EMS. This special issue seeks groundbreaking research articles that showcase the application of applied probability and statistics to interdisciplinary studies. This study offers Improved Metaheuristics with a Deep Learning Enabled Risk Assessment Model (IMDLRA-SES) for Smart Enterprise Systems. Using feature selection (FS) and DL models, the provided IMDLRA-SES technique estimates business risks. Preprocessing is used in the IMDLRA-SES technique to change the original financial data into a usable format. In addition, an FS technique based on oppositional lion swarm optimization (OLSO) is utilized to find the best subset of features. In addition, the presence or absence of financial hazards in firms is classified using the triple tree seed algorithm (TTSA) with a probabilistic neural network (PNN) model. The TTSA is used as a hyperparameter optimizer to improve the efficiency of the PNN-based categorization. An extensive set of experimental evaluations is performed on German and Australian credit datasets to illustrate the IMDLRA-SES model’s improved performance. The performance validation of the IMDLRA-SES model portrayed a superior accuracy value of 95.70% and 96.09% over existing techniques.

Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study

The increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. Our approach leverages a combination of supervised and unsupervised learning techniques to predict query execution times, optimize performance, and dynamically manage workloads. Unlike existing solutions that address specific optimization tasks in isolation, our framework provides a unified platform that supports real-time model inference and automatic database configuration adjustments based on workload patterns. A key contribution of our work is the integration of ML capabilities directly into the DBMS engine, enabling seamless interaction between the ML models and the query optimization process. This integration allows for the automatic retraining of models and dynamic workload management, resulting in substantial improvements in both query response times and overall system throughput. Our evaluations using the Transaction Processing Performance Council Decision Support (TPC-DS) benchmark dataset at scale factors of 100 GB, 1 TB, and 10 TB demonstrate a reduction of up to 42% in query execution times and a 74% improvement in throughput compared with traditional approaches. Additionally, we address challenges such as potential conflicts in tuning recommendations and the performance overhead associated with ML integration, providing insights for future research directions. This study is motivated by the need for autonomous tuning mechanisms to manage large-scale, heterogeneous workloads while answering key research questions, such as the following: (1) How can machine learning models be integrated into a DBMS to improve query optimization and workload management? (2) What performance improvements can be achieved through dynamic configuration tuning based on real-time workload patterns? Our results suggest that the proposed framework significantly reduces the need for manual database administration while effectively adapting to evolving workloads, offering a robust solution for modern large-scale data environments.

Enhancing Data Warehouse Security: An Encrypted Cued-Click-Points Authentication Approach

Text passwords and other traditional security measures have been the mainstay of current data warehouse content security techniques. Due to the growing computational capability of information systems, security requirements, especially for the selection of secure text passwords, have changed throughout time. Because database administrators are either unaware of these changes or are overburdened by the magnitude of the adjustments necessary to remedy them, these vulnerabilities are typically invisible to them. As a result, research on usable authentication schemes have picked up steam, with the goal of developing a secure authentication method using cued click points and encryption techniques for accessing a hybrid data warehouse. This scheme aims to enhance the security of data access by combining the unique characteristics of cued click points with encryption to create a robust authentication system. The project will involve designing and implementing the authentication scheme, testing its effectiveness in a hybrid data warehouse environment, and evaluating its performance in terms of security and usability. Overall, the project aims to provide a secure and user-friendly authentication solution for accessing sensitive data in a hybrid data warehouse setting. This scheme utilizes a combination of encryption techniques and user-specific click points to verify user identity and access to the data warehouse. It also discusses the potential benefits of this authentication scheme in terms of data security and user experience.

ACDRL: A constraint-guided dynamic reinforcement learning for index selection

Index selection aims to find the optimal index configuration for a certain workload. In recent years, the database committee has attempted to develop an intelligent index advisor for replacing database administrators (DBAs). Although some researches show that applying reinforcement learning (RL) to index selection is a significant and promising direction, many problems still exist in applying the RL agent to the index selection i.e. long training time, unstable performance, and limited state representation. To address these issues, we propose a constraint-guided dynamic RL for index selection. Specifically, our method has three major contributions: (i) To balance the space consumption and index configuration quality, we design a dynamic index state structure to avoid missing effective indexes in limited space consumption. (ii) To accelerate agent training, we design a constraint-guided method to largely reduce useless explorations. (iii) To keep the stability of the RL, we propose the trade-off strategy for value function design instead of using Q_network mindlessly. We test our ACDRL with two open-source benchmarks, TPC-H and TPC-DS. The experimental results show that our method outperforms state-of-the-art methods and could find the optimal index configurations with limited time consumption.

Architectural Design for Data Security in Cloud-based Big Data Systems

التصميم المعماري للنظام هو عملية إبداعية بعد كل شيء. المتطلبات غير الوظيفية وبنية البرامج لها علاقة قوية. يعد أمن النظام أحد الشواغل الكبيرة وهو حاجة ومتطلب غير وظيفي. يجب أن يعتمد الأسلوب والهيكل المعماري المختاران للنظام على متطلبات النظام غير الوظيفية. تُستخدم تقنيات البيانات الضخمة لتخزين كميات هائلة من البيانات التشغيلية في المجال العام. تم اقتراح مخططات هيكل وسلوك النظام التي تستخدم تصميمًا معماريًا آمنًا لنظام يعمل عبر السحابة. تم التحقيق في مكونات مختلفة من البيانات الضخمة في هذا العمل البحثي نظرًا لخطورة القضايا الأمنية التي تركز على الخصوصية والسرية. يعد تأمين البيانات عبر السحابة التي يتم الاستعانة بمصادر خارجية لها مصدر قلق متزايد. هناك مسؤولو قواعد بيانات ضارة من الخارج ومن الداخل يمكنهم الوصول إلى البيانات الخاصة والحساسة وتعديلها ، وبالتالي يمكن أن يكون مزود خدمة قاعدة البيانات مخادعًا. آليات الأمان مطلوبة بالضرورة لحماية البيانات الحساسة الموجودة على السحابات العامة. على مستوى التطبيق ، يضمن الحل المقترح خصوصية وسرية البيانات الموجودة على السحابة. تم إجراء اختبارات مكثفة ، وكانت نتائج هذا البحث كما يلي: كشفت النتائج المسجلة عن اختلافات ذات دلالة إحصائية في النص العادي ومحتويات الوسائط المتعددة (أي الصوت والفيديو) مع قياس الوقت بالمللي ثانية لمجموعات البيانات الصغيرة والمتوسطة والكبيرة.

Improving Database Performance with SQL Server Optimization Techniques

Database performance is critical for ensuring efficient data management and retrieval, particularly in environments that utilize SQL Server. As the complexity and size of databases increase, performance optimization becomes essential for maintaining responsiveness and reliability. This paper examines a range of SQL Server optimization techniques that can significantly enhance database performance. Key strategies include effective indexing, which improves query execution speed; query optimization, which refines SQL statements for better efficiency; and proper database configuration, which ensures that SQL Server is tuned for optimal resource utilization. Additionally, we explore the importance of regular maintenance tasks such as updating statistics and monitoring performance metrics to identify bottlenecks. By employing these techniques, database administrators can minimize latency, reduce resource consumption, and enhance overall system performance. Furthermore, this paper highlights the significance of ongoing performance assessments to adapt to evolving application demands and data growth. Ultimately, implementing these optimization strategies not only boosts SQL Server performance but also contributes to improved user experiences and organizational productivity.

Report: Establishment of a DNA Fingerprinting Database in the United Arab Emirates

DNA fingerprinting represents a pivotal advancement in forensic science since the 1980s. Recognizing its significance, the United Arab Emirates (UAE) implemented the first law pertaining to this technology through Federal Decree by Law No. (39) of 2023, titled ‘Concerning the Regulation of the Federal DNA Fingerprinting Database’. This law came into effect on 1 November 2023. The report will delve into the concept of DNA fingerprinting and its database, focusing on the administration of the Federal DNA Fingerprinting Database and its practical applications. The discussion will cover various aspects, including the origins of biological specimens, managing biological samples preservation and execution, the retention duration of DNA fingerprinting, and the measures in place to ensure legal safeguards for genetic fingerprint databases. This brief overview endeavours to offer a concise insight into the legal framework and operational factors concerning DNA fingerprinting in the context of recent legislative changes.

Revisiting Database Indexing for Parallel and Accelerated Computing: A Comprehensive Study and Novel Approaches

While the importance of indexing strategies for optimizing query performance in database systems is widely acknowledged, the impact of rapidly evolving hardware architectures on indexing techniques has been an underexplored area. As modern computing systems increasingly leverage parallel processing capabilities, multi-core CPUs, and specialized hardware accelerators, traditional indexing approaches may not fully capitalize on these advancements. This comprehensive experimental study investigates the effects of hardware-conscious indexing strategies tailored for contemporary and emerging hardware platforms. Through rigorous experimentation on a real-world database environment using the industry-standard TPC-H benchmark, this research evaluates the performance implications of indexing techniques specifically designed to exploit parallelism, vectorization, and hardware-accelerated operations. By examining approaches such as cache-conscious B-Tree variants, SIMD-optimized hash indexes, and GPU-accelerated spatial indexing, the study provides valuable insights into the potential performance gains and trade-offs associated with these hardware-aware indexing methods. The findings reveal that hardware-conscious indexing strategies can significantly outperform their traditional counterparts, particularly in data-intensive workloads and large-scale database deployments. Our experiments show improvements ranging from 32.4% to 48.6% in query execution time, depending on the specific technique and hardware configuration. However, the study also highlights the complexity of implementing and tuning these techniques, as they often require intricate code optimizations and a deep understanding of the underlying hardware architecture. Additionally, this research explores the potential of machine learning-based indexing approaches, including reinforcement learning for index selection and neural network-based index advisors. While these techniques show promise, with performance improvements of up to 48.6% in certain scenarios, their effectiveness varies across different query types and data distributions. By offering a comprehensive analysis and practical recommendations, this research contributes to the ongoing pursuit of database performance optimization in the era of heterogeneous computing. The findings inform database administrators, developers, and system architects on effective indexing practices tailored for modern hardware, while also paving the way for future research into adaptive indexing techniques that can dynamically leverage hardware capabilities based on workload characteristics and resource availability.

Comparative analysis of adverse events among intravesical drugs in bladder cancer: a real-world study on FAERS database

ABSTRACT Background Intravesical therapy is a commonly utilized treatment for non-muscle invasive bladder cancer (NMIBC). This study focuses on summarizing the signals of all intravesical drugs and aims to highlight the comprehensive differences in adverse events (AEs) between these drugs. Research design and methods We conducted pharmacovigilance data analysis based on the real-world big data from the Food and Drug Administration Adverse Event Reporting System (FAERS) database. Results We elucidated all signals compared with the overall FAERS database or other administration routes for Bacillus Calmette-Guerin (BCG), mitomycin, gemcitabine, valrubicin, and epirubicin. Notably, the distribution of reported AEs associated with intravesical therapy exhibited a noticeable inclination toward male patients. Furthermore, all five drugs demonstrated a disproportionate distribution in local AEs, particularly in renal and urinary disorders. Additionally, specific signals and findings were summarized for each individual drug. Finally, we highlighted the AEs that resulted in serious outcomes for each drug. Conclusion We have compiled an overview of the AEs tied to intravesical drugs whilst considering their individual distinctions. These insightful findings serve to enrich our comprehension of the safety profiles and potential risks linked to intravesical therapy.

OPTIMIZING SQL DATABASES FORBIG DATA WORKLOADS: TECHNIQUES AND BEST PRACTICES

In the era of big data, SQL databases face significant challenges in handling vast volumes of data efficiently. This article explores optimization techniques and best practices for enhancing the performance and scalability of SQL databases in handling big data workloads. The study addresses the significant challenges faced by traditional SQL databases, including scalability issues, performance bottlenecks, resource constraints, and data integration complexities. Through a comprehensive methodology involving literature review, case studies, expert interviews, and performance testing, the research identifies effective strategies such as indexing, partitioning, sharding, and caching. Findings from case studies in e-commerce and financial services sectors demonstrate substantial improvements in query performance and resource utilization, validating the practical benefits of these optimization techniques. The study underscores the importance of a multifaceted approach to database optimization, integrating both theoretical and practical insights to address the complexities of big data environments. By staying informed and adopting the latest optimization strategies, database administrators and IT professionals can ensure their SQL databases remain efficient, scalable, and capable of managing the increasing demands of large-scale data processing, ultimately enabling organizations to derive valuable insights from their data.

ADVANCED QUERY OPTIMIZATION IN SQL DATABASES FOR REAL-TIME BIG DATA ANALYTICS

This study investigates the effectiveness of advanced query optimization techniques in SQL databases, focusing on multi-level indexing, query rewriting, and dynamic query execution plans. The research employs a qualitative approach, gathering data from a variety of SQL databases characterized by large datasets typical of big data environments. Through structured interviews, focus groups, and observational methods, insights from database administrators highlight the practical benefits and challenges associated with implementing these techniques. The findings reveal significant improvements in query performance, with multi-level indexing reducing data retrieval times by approximately 40%, query rewriting decreasing execution times by 35%, and dynamic query execution plans enhancing resource utilization efficiency by 25%. These techniques were also praised for their ease of use, adaptability to different data types and query complexities, and overall reliability. This study contributes to the existing body of knowledge by providing a comprehensive analysis of the practical applications and performance enhancements offered by advanced query optimization methods in SQL databases, underscoring their value in managing large-scale, dynamic data environments.

K-Means Algorithm for Clustering Students Based on Areas of Expertise (A Case Study)

Every student of the Software Engineering Technology study program, the Computer and Informatics Department of the Politeknik Negeri Medan in the lecture process is required to take all the courses in the curriculum. From the courses in the curriculum, there are several subject groups that shape student expertise, such as Software Engineering, System Analyst, Database Administrator, and IT Entrepreneur. It is hoped that this expertise will later be used as a reference by students in carrying out their thesis at the end of lectures. The purpose of this study is to group students based on their respective expertise based on data processing of student course scores related to each skill. The data used is data on student scores for batches of 2020 and 2021 with a range of courses from semester 1 to semester 3. The data is tested by implementing the K-Means algorithm. The results of the tests that have been carried out show the grouping of students based on their expertise, with 7 times the number of iterations. Then, data testing was carried out with the RapidMiner application to get the results of the distribution of cluster members obtained, including 12 students occupying Software Engineer skills, 21 students with System Analyst skills, 5 students with Database Administrator skills, and 31 students with IT Entrepreneur skills, along with the distribution chart. Thus, the K-Means algorithm is quite good at grouping students based on their expertise

D-Bot: Database Diagnosis System using Large Language Models

Database administrators (DBAs) play an important role in managing database systems. However, it is hard and tedious for DBAs to manage vast database instances and give timely response (waiting for hours is intolerable in many online cases). In addition, existing empirical methods only support limited diagnosis scenarios, which are also labor-intensive to update the diagnosis rules for database version updates. Recently large language models (LLMs) have shown great potential in various fields. Thus, we propose D-Bot , an LLM-based database diagnosis system that can automatically acquire knowledge from diagnosis documents, and generate reasonable and well-founded diagnosis report (i.e., identifying the root causes and solutions) within acceptable time (e.g., under 10 minutes compared to hours by a DBA). The techniques in D-Bot include ( i ) offline knowledge extraction from documents, ( ii ) automatic prompt generation (e.g., knowledge matching, tool retrieval), ( iii ) root cause analysis using tree search algorithm, and ( iv ) collaborative mechanism for complex anomalies with multiple root causes. We verify D-Bot on real benchmarks (including 539 anomalies of six typical applications), and the results show D-Bot can effectively identify root causes of unseen anomalies and significantly outperforms traditional methods and vanilla models like GPT-4.

Integrated Pest Management Decision Support System (IPM-DSS) a tool to support management of tree diseases in Kenya

Tree diseases have been noted as one of the major causes of abnormalities and malformations on perennial plants. Tree disease research in Kenya has resulted in a list of diseases identified to cause significant losses on forest plantations. The need for management of such diseases have been on the increase. The Decision Support System (DSS) was envisioned to help stakeholders make decisions to manage tree diseases on farm and in tree plantations. Information was collected across Kenya on diseases affecting seed, seedlings, plantations, natural forests and post-harvest products in sawmills. The data was entered into excel sheets and organized for database administration on MySQL software. Over 20 major fungal species were identified and their biology, distribution and management determined. This information was further fed into a query system to allow different groups of stakeholders to access the information for diagnosis and control of tree diseases. Results showed that major tree diseases in Kenya were caused by fungi belonging to the families; Botryosphaeriaceae, Nectriaceae, and Amphisphaeriaceae. The data and information availed through this query system is being used in trend analysis for proactive control and management of tree diseases in Kenya. It will also be used for farmer advisory services through closest match selection of symptom appearance based on the tree species. The query system is an innovation that will also be used for Citizen Science Data Capture to aid Kenya Forestry Research Institute in detecting emerging diseases in real-time for phytosanitary measures. The identification of commonly occurring pathogens in commercial forestry species in the country has led to research towards their control and tree improvement for resistance to tree diseases. Future plantation establishment should be based on resistant species and high-quality germplasm for maximum survival and improved yields. Key words: Advisory services, Botryosphaeriaceae, citizen science, Decision Support System, Integrated Pest Management, tree diseases.

Aadhar Based Biometric Electronic Voting System

Flawless voting is guaranteed through the use of Electronic Voting Machines. It is crucial for citizens to have confidence in the security of their votes and to prevent any fraudulent activities. The primary objective of this initiative is to create a secure Electronic Voting Machine that utilizes fingerprint identification technology. To access fingerprints, the AADHAR card database is utilized. During elections, the authentication process for e-voting involves finger vein sensing, which allows voters to cast their votes after electronic ballot reset. Furthermore, the voting data and details of voters can be transmitted to the nearby Database Administration unit via WIFI. The fingerprint scanning feature is implemented to ensure security and prevent issues like fake or repeated voting. This system not only improves accuracy but also enhances the speed of the voting process

Securing the Digital Frontier: Strategies for Cloud Computing Security, Database Protection, and Comprehensive Penetration Testing

This research paper explores the effectiveness of integrated cybersecurity strategies, focusing on the amalgamation of cloud computing security, database protection, and penetration testing into a unified risk management framework. The primary aim is to evaluate how such integration impacts the overall cybersecurity posture of organizations, offering insights into mitigating cyber threats, unauthorized access, and data breaches. Employing a survey-based methodology, the study gathered data from 365 professionals across cloud computing, database administration, and cybersecurity fields. Through descriptive statistics and Partial Least Squares Structural Equation Modeling (PLS-SEM), the research explored the interrelations between various cybersecurity strategies and their collective influence on organizational resilience against cyber threats. The findings underscore the significant benefits of a holistic cybersecurity approach, revealing that penetration testing, robust database security measures, and strict adherence to cloud computing security requirements significantly reduce vulnerabilities and incidents of data breaches. Moreover, the study established that a unified risk management framework substantially enhances an organization's cybersecurity posture, highlighting the critical role of integrated security measures in fostering organizational resilience. The research confirms the hypothesis that incorporating cybersecurity strategies across different domains leads to a synergistic enhancement of security defenses, offering a more robust mechanism against the multifaceted nature of cyber threats. In conclusion, the study advocates for the adoption of a comprehensive, integrated approach to cybersecurity, emphasizing regular penetration testing, stringent database security protocols, and adherence to cloud computing security standards as essential components of a robust cybersecurity framework. This approach not only mitigates the risk of cyber threats but also strengthens organizational resilience, ensuring a secure digital environment for future challenges.

A Framework Design for Centralised Monitoring of Patient Disease Diagnosis for Better Improvement

Healthcare recommendation systems have garnered significant attention in recent times due to their capacity to improve patient outcomes and treatment. This literature review intends to assess the current state of patient healthcare referral systems by examining relevant studies, techniques, and findings. The report focuses on key research areas, challenges, and viable strategies for the future in the field of patient-centered health recommendation systems. Currently, healthcare administration is in high demand due to its significant advantages in managing hospitals or medical practices. Health management systems are increasingly affecting the entire world on a daily basis. The rising demand for healthcare is attributed to various factors, including the availability of healthcare solutions. The health prediction system is an online initiative designed to provide user support and advice. This study proposes a technology that allows consumers to receive immediate online health guidance from an intelligent healthcare system. The system encompasses a multitude of disorders and symptoms associated with different bodily systems. Data mining technologies can be utilized to identify the most probable disease associated with a patient's symptoms. By logging into the system, a doctor can retrieve and review their patient's information and reports within the doctor's module. Physicians have the ability to analyze the patient's browsing history and the specific information they are seeking, taking into account their medical prognosis. The doctor has access to his data. The database administrator has the ability to incorporate additional disease information, such as the type of disease and its symptoms. The data mining system runs based on the condition's name and symptoms. The administrator has access to the database including information on diseases and symptoms. Recommender systems employ diverse machine learning techniques in many domains, such as the healthcare recommendation system (HRS), to advise and promote services or entities to users. Due to the vast array of algorithms documented in the literature, the science of artificial intelligence is now widely employing machine learning techniques in various application domains, including the HRS. Nevertheless, the process of selecting an appropriate machine learning algorithm for a health recommender system seems to be time-consuming.

Application of LSTM Feature Extraction Model in the Construction of Opera Audio Database

Chinese traditional music plays a greater role in ideal society governed by rites and rituals. The musical culture of China is both ancient and modern. Throughout history, musicians have created several musical genres and used countless instruments. In order to appreciate Chinese traditional music, it is helpful to have a basic familiarity with three "keynotes": the classical traditional music of the elites; folk and ethnic music, and the most popular traditional Chinese instruments. In general, an opera audio database consists of audio recordings of opera performances. These recordings may be utilized for a variety of reasons, including research, education, and entertainment. To establish such a database, pertinent features must be extracted from the audio recordings to allow for effective storage, retrieval, and analysis of the data. Improvements in audio signal processing over the last several years have qualified for creating feature extraction methods that improve the quality and accuracy of the retrieved features. We first gather the audio recordings of opera performances using an appropriate recording device to build an opera audio database. Data preprocessing then eliminates noise and other undesired signals from the audio recordings to enhance the data quality. After that, to extract useful features from the audio recordings that may be utilized to define the sound's rates, the Long Short Term Memory (LSTM) technique is used. The components must then be organized and stored in a database for fast retrieval and analysis once extracted. A suitable database management system may be used to build the database. The database should be created to make it simple to search and filter the data using different criteria. The study results stated that building an opera audio database using novel feature extraction approaches is a challenging process that requires an understanding of opera music, database administration, and audio signal processing. The experimental results stated that the proposed model has provided an accuracy of 96%.