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Adaptive Security Reliability Meta Monitoring Framework for Cybersecurity Detection Systems **DOI :****10.17577/IJERTV15IS030073** Download Full-Text PDF Cite this Publication Prof. T B Dharmaraj, Mathan Raj A, M. Hemalatha, Arunajayan A P, Iniyavan M, Madhu Priya V R, 2026, Adaptive Security Reliability Meta Monitoring Framework for Cybersecurity Detection Systems, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 15, Issue 03 , March – 2026 * **Open Access** * Article Download / Views: 0 * **Authors :** Prof. T B Dharmaraj, Mathan Raj A, M. Hemalatha, Arunajayan A P, Iniyavan M, Madhu Priya V R * **Paper ID :** IJERTV15IS030073 * **Volume & Issue : ** Volume 15, Issue 03 , March – 2026 * **Published (First Online):** 14-03-2026 * **ISSN (Online) :** 2278-0181 * **Publisher Name :** IJERT * **License:** This work is licensed under a Creative Commons Attribution 4.0 International License __ PDF Version View __ Text Only Version #### Adaptive Security Reliability Meta Monitoring Framework for Cybersecurity Detection Systems Prof. T B Dharmaraj Head of the Department (Mentor) Department of Information Technology PPG Institute of Technology, Tamil Nadu, India Mathan Raj A Department of Information Technology PPG Institute of Technology, Tamil Nadu, India M. Hemalatha Assistant Professor (Mentor) Department of Information Technology PPG Institute of Technology, Tamil Nadu, India Arunajayan A P Department of Information Technology PPG Institute of Technology, Tamil Nadu, India Iniyavan M Department of Information Technology PPG Institute of Technology, Tamil Nadu, India Madhu Priya V R Department of Information Technology PPG Institute of Technology, Tamil Nadu, India Abstract – Cybersecurity detection systems such as intrusion detection systems and endpoint detection platforms may lose effectiveness over time due to evolving threats and system drift. This paper proposes the Adaptive Security Reliability Meta Monitoring Framework (ASRM), a monitoring layer that continuously evaluates detection reliability using drift analysis, entropy monitoring, blind spot probability modeling, and adver- sarial simulation techniques. The framework generates a Security Reliability Score (SRS) that quantifies the operational reliability of enterprise security monitoring systems. Experimental evalu- ation demonstrates that the proposed framework can identify reliability degradation and improve cybersecurity resilience. Index Terms – Cybersecurity, Detection Reliability, Drift Anal- ysis, Blind Spot Detection, Security Monitoring, Machine Learn- ing 1. INTRODUCTION Cybersecurity infrastructures depend on detection systems such as intrusion detection systems, endpoint detection plat- forms, and security information and event management plat- forms to identify malicious activities. However, the effective- ness of these systems may degrade over time due to evolving attack techniques, configuration changes, and incomplete de- tection coverage. Most existing security tools focus primarily on threat de- tection rather than evaluating the reliability of the detection infrastructure itself. As a result, monitoring blind spots may remain undetected, increasing the risk of successful cyber attacks. To address this problem, this paper proposes the Adaptive Security Reliability Meta Monitoring Framework (ASRM), a monitoring layer that continuously evaluates the reliability of cybersecurity detection systems using statistical analysis and adversarial simulation techniques. 2. RELATED WORK Intrusion detection systems are widely used to detect mali- cious activities in network environments. Traditional signature-based detection approaches rely on predefined attack signa- tures and often fail to detect unknown threats. Machine learning techniques have been introduced to im- prove anomaly detection in cybersecurity environments. How- ever, most existing research focuses on detecting attacks rather than evaluating the reliability of detection systems. Security Information and Event Management platforms pro- vide centralized monitoring by aggregating logs from multiple security tools. Despite their usefulness, SIEM systems typi- cally lack mechanisms to measure detection reliability. The proposed ASRM framework addresses this gap by introducing a reliability monitoring layer that evaluates de- tection effectiveness using statistical analysis and adversarial simulations. 3. SYSTEM ARCHITECTURE The ASRM framework operates as a meta monitoring layer integrated with existing cybersecurity detection infrastructure. The framework collects telemetry data from intrusion detection systems, endpoint detection platforms, firewalls, authentication systems, and SIEM platforms. The collected logs are normalized and processed through multiple reliability evaluation modules including drift analysis, entropy monitoring, blind spot detection, and adversarial simu- lation. The outputs of these modules are combined to compute a Security Reliability Score. 4. SYSTEM DATA PREPARATION Security telemetry data is collected from multiple sources including IDS, EDR, firewalls, authentication logs, and SIEM platforms. The collected data is normalized to ensure consis- tent representation across different sources. Data preprocessing includes removal of duplicate records, handling missing values, and classification of events based on severity levels. The processed dataset is stored in a centralized monitoring database for reliability evaluation. Fig. 1. Adaptive Security Reliability Meta Monitoring Framework Architec- ture 5. RELIABILITY METRICS The ASRM framework evaluates detection effectiveness using statistical reliability metrics. 1. Detection Drift Score Measures deviations between current detection patterns and historical baseline behavior. 2. Coverage Score Represents the percentage of simulated threats successfully detected by security monitoring systems. 3. Entropy Score Measures the diversity and randomness of detection alerts. 4. Adversarial Simulation Score Evaluates detection capability using simulated attack sce- narios. 5. Security Reliability Score The overall reliability of the detection infrastructure is represented by the Security Reliability Score. SRS = Wd Β· D + Wc Β· C + We Β· E + Wa Β· A (1) where * D = Detection Drift Score * C = Coverage Score * E = Entropy Score * A = Adversarial Simulation Score * Wd, Wc, We, Wa = weighting factors The weighting factors satisfy: Wd + Wc + We + Wa = 1 (2) 6. SYSTEM IMPLEMENTATION The ASRM framework was implemented using Python for statistical analysis and reliability computation. Log processing was performed using the Pandas and NumPy libraries, while entropy and drift calculations were implemented using SciPy. The monitoring dashboard was developed using a lightweight web interface for visualization of reliability scores. 7. EXPERIMENTAL EVALUATION The proposed framework was evaluated using publicly available cybersecurity datasets including CICIDS2017 and UNSW-NB15. A. Evaluation Metrics * Detection Drift Score * Coverage Score * Entropy Score * Adversarial Detection Rate TABLE I Reliability Evaluation Results Metric Value Detection Drift Score 84 Coverage Score 88 Entropy Score 79 Adversarial Detection Rate 85 Security Reliability Score (SRS) 84 8. CONCLUSION This paper presented the Adaptive Security Reliability Monitor framework for evaluating the reliability of enterprise cybersecurity monitoring systems. The proposed approach introduces reliability-centric monitoring using drift analysis, entropy monitoring, blind spot detection, and adversarial sim- ulation. The framework generates aSecurity Reliability Score that provides a measurable indicator of monitoring effectiveness. By identifying reliability degradation and monitoring blind spots, the ASRM framework improves cybersecurity resilience and situational awareness. FUTURE WORK Future work will focus on integrating real-time machine learning models to improve detection reliability evaluation. Additional adversarial simulation scenarios will be developed to test monitoring resilience in large-scale enterprise and cloud environments. ACKNOWLEDGMENT The authors thank Prof T B Dharmaraj and M. Hemalatha for their guidance and support during the development of this research work. REFERENCES 1. NIST, Guide to Intrusion Detection and Prevention Systems, Special Publication 800-94, 2007. 2. C. Kruegel, F. Valeur, and G. Vigna, Intrusion Detection and Correla- tion: Challenges and Solutions. Springer, 2005. 3. R. Sommer and V. Paxson, Outside the closed world: On using machine learning for network intrusion detection, IEEE Symposium on Security and Privacy, 2010. 4. S. Axelsson, The base-rate fallacy and its implications for intrusion detection, ACM CCS, 1999. 5. OWASP Foundation, OWASP Top Ten Web Application Security Risks, 2021. 6. T. Lunt, A survey of intrusion detection techniques, Computers and Security, 1993. 7. W. Lee and S. Stolfo, Data mining approaches for intrusion detection, USENIX Security Symposium, 1998. 8. M. Roesch, Snort: Lightweight intrusion detection for networks, USENIX LISA Conference, 1999. 9. D. Denning, An intrusion-detection model, IEEE Transactions on Software Engineering, 1987. 10. M. Tavallaee et al., A detailed analysis of the KDD CUP 99 data set, IEEE CISDA, 2009. 11. I. Sharafaldin et al., Toward generating a new intrusion detection dataset, ICISSP, 2018. 12. NSA, Defensive Cyber Operations Guidance, NSA Cybersecurity Di- rectorate, 2022. 13. M. Ring, D. Wunderlich, D. Scheuring, D. Landes, and A. Hotho, A survey of network-based intrusion detection data sets, Computers & Security, vol. 86, pp. 147167, 2019. 14. I. Sharafaldin, A. Habibi Lashkari, and A. Ghorbani, Toward generating a new intrusion detection dataset and intrusion traffic characterization, in Proc. International Conference on Information Systems Security and Privacy, 2018. 15. N. Moustafa and J. Slay, UNSW-NB15: A comprehensive data set for network intrusion detection systems, in Military Communications and Information Systems Conference, 2015. 16. A. Javaid, Q. Niyaz, W. Sun, and M. Alam, A deep learning approach for network intrusion detection system, in Proc. IEEE International Conference on Computing, Networking and Communications, 2016. 17. S. Berman, A. Buczak, J. Chavis, and C. Corbett, A survey of deep learning methods for cyber security, Information, vol. 10, no. 4, 2019. 18. A. Khraisat, I. Gondal, P. Vamplew, and J. Kamruzzaman, Survey of intrusion detection systems: Techniques, datasets, and challenges, Cybersecurity, vol. 2, no. 1, 2019. Fig. 2. Data Flow of the Adaptive Security Reliability Meta Monitoring Framework ______________

Adaptive Security Reliability Meta Monitoring Framework for Cybersecurity Detection Systems View Abstract & download full text of Adaptive Security Reliability Meta Monitoring Framework for Cyb...

#Volume #15, #Issue #03 #(March #2026)

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Adaptive Security Reliability Meta Monitoring Framework for Cybersecurity Detection Systems **DOI :****10.17577/IJERTV15IS030073** Download Full-Text PDF Cite this Publication Prof. T B Dharmaraj, Mathan Raj A, M. Hemalatha, Arunajayan A P, Iniyavan M, Madhu Priya V R, 2026, Adaptive Security Reliability Meta Monitoring Framework for Cybersecurity Detection Systems, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 15, Issue 03 , March – 2026 * **Open Access** * Article Download / Views: 0 * **Authors :** Prof. T B Dharmaraj, Mathan Raj A, M. Hemalatha, Arunajayan A P, Iniyavan M, Madhu Priya V R * **Paper ID :** IJERTV15IS030073 * **Volume & Issue : ** Volume 15, Issue 03 , March – 2026 * **Published (First Online):** 14-03-2026 * **ISSN (Online) :** 2278-0181 * **Publisher Name :** IJERT * **License:** This work is licensed under a Creative Commons Attribution 4.0 International License __ PDF Version View __ Text Only Version #### Adaptive Security Reliability Meta Monitoring Framework for Cybersecurity Detection Systems Prof. T B Dharmaraj Head of the Department (Mentor) Department of Information Technology PPG Institute of Technology, Tamil Nadu, India Mathan Raj A Department of Information Technology PPG Institute of Technology, Tamil Nadu, India M. Hemalatha Assistant Professor (Mentor) Department of Information Technology PPG Institute of Technology, Tamil Nadu, India Arunajayan A P Department of Information Technology PPG Institute of Technology, Tamil Nadu, India Iniyavan M Department of Information Technology PPG Institute of Technology, Tamil Nadu, India Madhu Priya V R Department of Information Technology PPG Institute of Technology, Tamil Nadu, India Abstract – Cybersecurity detection systems such as intrusion detection systems and endpoint detection platforms may lose effectiveness over time due to evolving threats and system drift. This paper proposes the Adaptive Security Reliability Meta Monitoring Framework (ASRM), a monitoring layer that continuously evaluates detection reliability using drift analysis, entropy monitoring, blind spot probability modeling, and adver- sarial simulation techniques. The framework generates a Security Reliability Score (SRS) that quantifies the operational reliability of enterprise security monitoring systems. Experimental evalu- ation demonstrates that the proposed framework can identify reliability degradation and improve cybersecurity resilience. Index Terms – Cybersecurity, Detection Reliability, Drift Anal- ysis, Blind Spot Detection, Security Monitoring, Machine Learn- ing 1. INTRODUCTION Cybersecurity infrastructures depend on detection systems such as intrusion detection systems, endpoint detection plat- forms, and security information and event management plat- forms to identify malicious activities. However, the effective- ness of these systems may degrade over time due to evolving attack techniques, configuration changes, and incomplete de- tection coverage. Most existing security tools focus primarily on threat de- tection rather than evaluating the reliability of the detection infrastructure itself. As a result, monitoring blind spots may remain undetected, increasing the risk of successful cyber attacks. To address this problem, this paper proposes the Adaptive Security Reliability Meta Monitoring Framework (ASRM), a monitoring layer that continuously evaluates the reliability of cybersecurity detection systems using statistical analysis and adversarial simulation techniques. 2. RELATED WORK Intrusion detection systems are widely used to detect mali- cious activities in network environments. Traditional signature-based detection approaches rely on predefined attack signa- tures and often fail to detect unknown threats. Machine learning techniques have been introduced to im- prove anomaly detection in cybersecurity environments. How- ever, most existing research focuses on detecting attacks rather than evaluating the reliability of detection systems. Security Information and Event Management platforms pro- vide centralized monitoring by aggregating logs from multiple security tools. Despite their usefulness, SIEM systems typi- cally lack mechanisms to measure detection reliability. The proposed ASRM framework addresses this gap by introducing a reliability monitoring layer that evaluates de- tection effectiveness using statistical analysis and adversarial simulations. 3. SYSTEM ARCHITECTURE The ASRM framework operates as a meta monitoring layer integrated with existing cybersecurity detection infrastructure. The framework collects telemetry data from intrusion detection systems, endpoint detection platforms, firewalls, authentication systems, and SIEM platforms. The collected logs are normalized and processed through multiple reliability evaluation modules including drift analysis, entropy monitoring, blind spot detection, and adversarial simu- lation. The outputs of these modules are combined to compute a Security Reliability Score. 4. SYSTEM DATA PREPARATION Security telemetry data is collected from multiple sources including IDS, EDR, firewalls, authentication logs, and SIEM platforms. The collected data is normalized to ensure consis- tent representation across different sources. Data preprocessing includes removal of duplicate records, handling missing values, and classification of events based on severity levels. The processed dataset is stored in a centralized monitoring database for reliability evaluation. Fig. 1. Adaptive Security Reliability Meta Monitoring Framework Architec- ture 5. RELIABILITY METRICS The ASRM framework evaluates detection effectiveness using statistical reliability metrics. 1. Detection Drift Score Measures deviations between current detection patterns and historical baseline behavior. 2. Coverage Score Represents the percentage of simulated threats successfully detected by security monitoring systems. 3. Entropy Score Measures the diversity and randomness of detection alerts. 4. Adversarial Simulation Score Evaluates detection capability using simulated attack sce- narios. 5. Security Reliability Score The overall reliability of the detection infrastructure is represented by the Security Reliability Score. SRS = Wd Β· D + Wc Β· C + We Β· E + Wa Β· A (1) where * D = Detection Drift Score * C = Coverage Score * E = Entropy Score * A = Adversarial Simulation Score * Wd, Wc, We, Wa = weighting factors The weighting factors satisfy: Wd + Wc + We + Wa = 1 (2) 6. SYSTEM IMPLEMENTATION The ASRM framework was implemented using Python for statistical analysis and reliability computation. Log processing was performed using the Pandas and NumPy libraries, while entropy and drift calculations were implemented using SciPy. The monitoring dashboard was developed using a lightweight web interface for visualization of reliability scores. 7. EXPERIMENTAL EVALUATION The proposed framework was evaluated using publicly available cybersecurity datasets including CICIDS2017 and UNSW-NB15. A. Evaluation Metrics * Detection Drift Score * Coverage Score * Entropy Score * Adversarial Detection Rate TABLE I Reliability Evaluation Results Metric Value Detection Drift Score 84 Coverage Score 88 Entropy Score 79 Adversarial Detection Rate 85 Security Reliability Score (SRS) 84 8. CONCLUSION This paper presented the Adaptive Security Reliability Monitor framework for evaluating the reliability of enterprise cybersecurity monitoring systems. The proposed approach introduces reliability-centric monitoring using drift analysis, entropy monitoring, blind spot detection, and adversarial sim- ulation. The framework generates aSecurity Reliability Score that provides a measurable indicator of monitoring effectiveness. By identifying reliability degradation and monitoring blind spots, the ASRM framework improves cybersecurity resilience and situational awareness. FUTURE WORK Future work will focus on integrating real-time machine learning models to improve detection reliability evaluation. Additional adversarial simulation scenarios will be developed to test monitoring resilience in large-scale enterprise and cloud environments. ACKNOWLEDGMENT The authors thank Prof T B Dharmaraj and M. Hemalatha for their guidance and support during the development of this research work. REFERENCES 1. NIST, Guide to Intrusion Detection and Prevention Systems, Special Publication 800-94, 2007. 2. C. Kruegel, F. Valeur, and G. Vigna, Intrusion Detection and Correla- tion: Challenges and Solutions. Springer, 2005. 3. R. Sommer and V. Paxson, Outside the closed world: On using machine learning for network intrusion detection, IEEE Symposium on Security and Privacy, 2010. 4. S. Axelsson, The base-rate fallacy and its implications for intrusion detection, ACM CCS, 1999. 5. OWASP Foundation, OWASP Top Ten Web Application Security Risks, 2021. 6. T. Lunt, A survey of intrusion detection techniques, Computers and Security, 1993. 7. W. Lee and S. Stolfo, Data mining approaches for intrusion detection, USENIX Security Symposium, 1998. 8. M. Roesch, Snort: Lightweight intrusion detection for networks, USENIX LISA Conference, 1999. 9. D. Denning, An intrusion-detection model, IEEE Transactions on Software Engineering, 1987. 10. M. Tavallaee et al., A detailed analysis of the KDD CUP 99 data set, IEEE CISDA, 2009. 11. I. Sharafaldin et al., Toward generating a new intrusion detection dataset, ICISSP, 2018. 12. NSA, Defensive Cyber Operations Guidance, NSA Cybersecurity Di- rectorate, 2022. 13. M. Ring, D. Wunderlich, D. Scheuring, D. Landes, and A. Hotho, A survey of network-based intrusion detection data sets, Computers & Security, vol. 86, pp. 147167, 2019. 14. I. Sharafaldin, A. Habibi Lashkari, and A. Ghorbani, Toward generating a new intrusion detection dataset and intrusion traffic characterization, in Proc. International Conference on Information Systems Security and Privacy, 2018. 15. N. Moustafa and J. Slay, UNSW-NB15: A comprehensive data set for network intrusion detection systems, in Military Communications and Information Systems Conference, 2015. 16. A. Javaid, Q. Niyaz, W. Sun, and M. Alam, A deep learning approach for network intrusion detection system, in Proc. IEEE International Conference on Computing, Networking and Communications, 2016. 17. S. Berman, A. Buczak, J. Chavis, and C. Corbett, A survey of deep learning methods for cyber security, Information, vol. 10, no. 4, 2019. 18. A. Khraisat, I. Gondal, P. Vamplew, and J. Kamruzzaman, Survey of intrusion detection systems: Techniques, datasets, and challenges, Cybersecurity, vol. 2, no. 1, 2019. Fig. 2. Data Flow of the Adaptive Security Reliability Meta Monitoring Framework ______________

Adaptive Security Reliability Meta Monitoring Framework for Cybersecurity Detection Systems View Abstract & download full text of Adaptive Security Reliability Meta Monitoring Framework for Cyb...

#Volume #15, #Issue #03 #(March #2026)

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Digital Department Logbook & Academic Management System **DOI :****10.17577/IJERTV15IS030350** Download Full-Text PDF Cite this Publication Jeevanantham G, Amrutha S, Aswini R, Ramya S, Visakh P J, 6 Yusvaanth A S, 2026, Digital Department Logbook & Academic Management System, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 15, Issue 03 , March – 2026 * **Open Access** * Article Download / Views: 0 * **Authors :** Jeevanantham G, Amrutha S, Aswini R, Ramya S, Visakh P J, 6 Yusvaanth A S * **Paper ID :** IJERTV15IS030350 * **Volume & Issue : ** Volume 15, Issue 03 , March – 2026 * **Published (First Online):** 14-03-2026 * **ISSN (Online) :** 2278-0181 * **Publisher Name :** IJERT * **License:** This work is licensed under a Creative Commons Attribution 4.0 International License __ PDF Version View __ Text Only Version #### Digital Department Logbook & Academic Management System (1)Jeevanantham G, (2) Amrutha S, (3) Aswini R, (4) Ramya S, (5) Visakh P J, (6) Yusvaanth A S (1)Assistant Professor (Senior Grade), (12345)Department of Computer Science and Engineering (12345) Nehru Institute of Engineering and Technology Coimbatore -641105 Abstract : Educational institutions handle a huge amount of academic data on a regular basis. These include students’ attendance, internal marks, assignments, evaluation of seminars, and regular academic activities. Most of these academic activities are handled manually, either by maintaining a paper log or by using spreadsheet software. Handling academic records manually often causes problems like computational mistakes, redundancy of data, problems in retrieving data, and lack of transparency for students. Faculty members often spend more time managing records instead of devoting their valuable time to teaching and academic improvements. To overcome these problems, this project aims to implement a Digital Department Logbook & Academic Management System (DDLAMS). DDLAMS is a centralized academic management system that helps manage academic records efficiently within the academic department. Faculty members can easily manage students’ attendance, internal marks, assignments, and regular academic activities through an online interface. Students can also access their records. The system is built using the MERN stack, which includes MongoDB, Express.js, React.js, and Node.js. These technologies enable the system to offer a user interface, security, and effective management of data. Role-Based Access Control (RBAC) is used to offer different access levels for administrators, faculty members, and students. JWT and bcrypt are security techniques used for authenticating and encrypting user data. The implementation of this system ensures transparency, reduces administrative burden, and ensures effective management of academic records. It contributes to the digital transformation of educational institutions by replacing traditional methods with an effective and secure web-based system. Keywords : Academic Management System, Digital Logbook, MERN Stack, Attendance Management, Student Record Management, Role-Based Access Control 1. INTRODUCTION : Educational institutions play an important role in maintaining and managing the academic records of the students. Academic records include student attendance, internal marks, assignment marks, seminar marks, and daily class activities. Managing academic records is important for monitoring the performance of the students and for easy management of academic institutions. For many academic departments, records are managed using traditional methods such as maintaining registers and using spreadsheet files. Although this has been followed for many years, it has many disadvantages. For example, manual records may lead to many human errors, and performing many calculations for attendance and marks may be time- consuming. Managing many registers may not be useful for maintaining and accessing old academic records. Another important factor is the lack of transparency for the students. For example, the attendance and internal marks are not available for the students unless they are announced. This may lead to many confusions and misconceptions about the performance of the students. However, with the advancement of web technologies and digital systems, educational institutions can now use modern technologies to efficiently manage academic records. A digital academic management system can efficiently manage all the data of the departments, as well as automate various processes such as calculating attendance and evaluating marks. A Digital Department Logbook & Academic Management System (DDLAMS) can be used as a solution for the academic departments of educational institutions. The digital logbook will allow the faculty members to efficiently manage the academic records of the departments through a web-based platform. Students can also view their academic records through a digital platform. 2. PROBLEM STATEMENT : Academic departments deal with a lot of information regarding student activities and performance. Still, the traditional methods are used for maintaining these records. There are certain issues with the traditional method of maintaining academic records. The faculty members have to maintain the records of student attendance and marks manually in registers. The calculation of attendance percentages and internal marks for every student is time-consuming and increases the chances of errors. There is also a problem of storage. The academic records are not centrally stored; instead, the information is stored in different registers or files. This creates a problem when the information is required for some purpose. For instance, during academic audits or reviews in the academic departments, the collection of information from the registers or files takes a lot of time. Students are also facing certain issues as the information is not provided to them in a transparent manner. The student does not have the accessibility to the academic records. Therefore, there is a need for a digital solution that can assist in the automation of academic records. 3. OBJECTIVES OF THE STUDY : The main objective of the Digital Department Logbook & Academic Management System is to create an efficient digital platform for the management of academic information. The specific objectives are as follows: * Digitize department logbook records * Reduce paperwork and administration * Provide central storage of academic information * Improve accuracy in attendance and mark calculations * Increase transparency for students * Provide secure access through authentication * Simplify academic reporting and information retrieval By achieving these objectives, it is possible to significantly enhance the efficiency of departmental academic management. 4. LITERATURE SURVEY : Several research works and systems have been developed to enhance academic records management in learning institutions. The Student Information System (SIS) is widely used for storing student information, including personal information, course registration, and results. However, these systems have been mainly used for administrative purposes. Learning Management Systems (LMS) like Moodle and Google Classroom have also been widely used for academic records management. These systems enable learning institutions to conduct online learning activities. They allow tutors to upload assignments, track student performance, and monitor their progress. However, these systems have mainly been used for online learning. Some learning institutions have also used automated attendance management systems that make use of RFID cards, biometric identification, or QR codes. However, these systems have mainly been used for attendance management. Several research works have also been conducted on academic records management, suggesting that integrating different academic functions into one digital platform can enhance efficiency in academic records management, thus reducing data duplication. The Digital Department Logbook & Academic Management System is a platform that combines different academic functions, including attendance managemnt, academic evaluation, and departmental records management, into one platform. 5. EXISTING SYSTEM : In the existing system, the academic records are maintained manually using a paper register or a spreadsheet file. The faculty members record the attendances during each class session and calculate the percentage of attendances manually. Internal assessment marks and assignment scores are also maintained manually. This calculation is often repeated many times, which may cause errors. Another drawback in the existing system is that the data is not stored centrally. Each faculty member may be maintaining the data separately, which may be a problem when the data is required. 6. PROPOSED SYSTEM : The proposed Digital Department Logbook & Academic Management System seeks to address the limitations of the current manual system. The proposed system allows faculty members to record student attendance, update marks, and maintain academic records electronically using a web-based platform. The data collected will be stored in a centralized database. Students can use the proposed system to view their attendance percentage, internal marks, and academic performance. The proposed system ensures that the student has read-only privileges. This proposed system is more efficient and ensures the accuracy of academic record management. 7. SYSTEM ARCHITECTURE : The Digital Department Logbook & Academic Management System (DDLAMS) is developed based on a three-tier architecture model. In this architecture model, there are three layers in which the system is implemented: the Frontend Layer, the Backend Layer, and the Database Layer. Each layer has its own functions, which communicate with other layers for an efficient system. Frontend Layer: The frontend layer is responsible for providing an interface for users. In DDLAMS, the frontend is implemented by using React.js, which is an open-source JavaScript library for building modern web applications. React.js is widely used for developing robust web applications. The frontend is considered the bridge between users and the system. In DDLAMS, different users have different interfaces for different functionalities. Faculty members have different interfaces for different functionalities, such as attendance, internal marks, uploading assignment marks, and daily academic log maintenance. Similarly, different interfaces are provided for different users, such as for students and administrators. Faculty members can maintain attendance records, internal marks, upload their assignment marks, and maintain their daily academic log through the frontend provided by DDLAMS. Similarly, students can log in to the DDLAMS system and view their attendance records, internal marks, and academic log maintained by their faculty members. Additionally, React.js helps in updating the user interface dynamically without reloading the entire page. This improves the performance of the application. Backend Layer : The backend layer is responsible for handling the business logic of the application, which includes processing the data requests received from the frontend layer. In this project, Node.js and Express.js have been used for developing the backend layer. Node.js Node.js is a runtime environment that helps in running JavaScript on the server side. Node.js is known for its high performance, which enables it to handle multiple requests at any given time. Express.js Express.js is a lightweight web development framework that is built on Node.js. Express.js is known for its simplicity in developing web applications. The backend layer has many important tasks to perform, including processing user requests, validating data, authenticating users, and communicating with the database. For example, when a user records their attendance on the frontend layer, the request is sent to the backend server for processing. The request is processed, and the required information is retrieved from the database. The processed information is then sent back to the frontend layer. The second important task that is performed by the backend layer is related to user authentication and authorization. The verification of users identity is performed by this system before allowing them to access the features provided by the application. Database Layer : The database layer is responsible for storing all the academic information that is used by the system. In this project, MongoDB is used as the database management system. MongoDB is a NoSQL database management system that stores information in document form. In this project, academic information is stored in document form, which is very flexible for storing large amounts of information in an efficient manner. The academic information stored in the database includes information about students, staff members, attendance records, marks for assignments, evaluation of seminars, daily academic information, etc. Each type of academic information is stored separately in the database. In addition, MongoDB also supports the efficient retrieval of information, which is very beneficial for displaying academic information to users of the system. The records of all the activities performed in each department of the university can be stored securely in the central database, which can be retrieved when needed. 8. SYSTEM DESIGN : System design is another significant step in the development of the Digital Department Logbook & Academic Management System (DDLAMS). The main aim of the system design is to create an efficient system so that the Digital Department Logbook & Academic Management System can run efficiently and meet the needs of the users. The main aim of the system design is to create a user- friendly interface and a database system for the users. The Digital Department Logbook & Academic Management System is designed for multiple users, including the administrator, faculty members, and students. The users have different roles and permissions to use the system. To allow the users to use the system efficiently, the Digital Department Logbook & Academic Management System provides a separate dashboard for each type of user. The administrator dashboard allows the administrator to manage the users of the system, add or remove users from the system, and manage the settings of the system. The administrator also ensures that the academic records are maintained efficiently within the system. The faculty dashboard has been designed in such a manner that teachers can easily carry out their academic tasks. Using this dashboard, teachers can easily record attendance, internal marks, assignment marks, and daily academic logs. For this purpose, simple forms and input fields have been provided, ensuring accurate input of information. The student dashboard has been designed for the purpose of providing students with accurate information regarding their academic performance. Using this dashboard, students can easily keep track of their attendance percentages, internal marks, assignment marks, and seminar marks. This will help create transparency and provide accurate information to the students. While designing this system, it was ensured that there was a smooth flow of information between the frontend, backend, and database layers. Information provided by teachers is stored in the database and then presented to the students using the user interface. 9. METHODOLOGY : The methodology for the development of the Digital Department Logbook & Academic Management System is a structured approach to ensure the system meets the requirements of the users and runs effectively. The methodology for the development of the system involves a number of steps. Requirement Analysis The first step in the development of the system is the requirement analysis. In this step, the problems associated with the existing system are analyzed.Faculty members find it difficult to maintain registers and calculate attendance or internal marks. Similarly, students do not have easy access to their academic records. Based on the problems of the existing system, the requirements of the digital academic management system are analyzed. System Design After the analysis of the requirements of the digital academic management system, the next step is the system design. This phase of the system design focuses on the architecture of the system. The architecture of the system is designed to meet the requirements of the users. The design of the system focuses on the database of the system. Development This is the stage where the system will be implemented using the various technologies of the MERN stack. The front-end of the system will be built using React.js, while the back-end will be built using Node.js and Express.js. The data storage of the academic information will be handled by MongoDB. Testing This stage is used to test the functions of the system to ensure they are working properly. This is the stage where errors and bugs are found and corrected. The testing of the system will be used to ensure the system will be able to record attendance, store academic information, and control user access. Deployment This is the stage where the system will be deployed to the department. The faculty members of the department will be able to access the system through a web browser. This is the stage where the system will be ready for the real world. 10. SYSTEM MODULES : The Digital Department Logbook & Academic Management System has various modules to enable each user to perform a particular function effectively. This helps in the organization of the system. Staff Module : The Staff Module is for the faculty members of the college. Faculty members are responsible for the management of the academic records. This module helps the teachers to maintain the student records effectively. Functions of the Staff Module: Recording the attendance of the students Recording the internal assessment marks of the students Uploading the marks of the assignments Evaluating the seminar presentations of the students Maintaining the class logs This module helps the teachers to maintain the academic records of the students. The teachers do not have to maintain the records in a physical logbook. Student Module : This module allows students to access their academic records using a login ID. The students have read access to the information, meaning they can only read the information but cannot edit it. Students can read: Attendance percentage Internal assessment marks Assignment results Seminar evaluation scores This module increases transparency for the students. 11. DATABASE DESIGN : The role of database design in the efficient storage and management of academic data cannot be overstated. For this project, MongoDB is used as the database management system. MongoDB MongoDB is a database management system that belongs to the category of NoSQL databases. This means that the data is stored in a document format. Database Design There are a number of collections in the database, each used to store different data. Users Collection In this collection, the login information of the administrator, faculty, and students is stored. Students Collection In this collection, the information of the students, including student ID, name, and department, is stored. Attendance Collection In this collection, the daily attendance information of the students is stored. Assignments Collection This collection holds information about the assignments and the marks for each student. Internal Marks Collection This collection holds information about the internal assessment data for each student. Seminar Evaluations Collection This collection holds the marks for the student seminar evaluations. 1. ER DIAGRAM EXPLANATION : Entity Relationship Diagram The Entity Relationship (ER) Diagram shows the structure of the database and the relationships among various entities of the system. The major entities of the system include Student, Staff, Attendance, Assignments, Internal Marks, and Seminar Evaluations. Student The Student entity includes various attributes like student ID, name, department, etc. Every student has his/her attendance, assignment marks, and internal marks. Staff Staff members are the faculty of the college. They handle the attendance of the students, the assignment marks of the students, etc. Attendance This entity stores the attendance of the students on a daily basis. This entity is related to the Student entity through the student ID. Assignments This entity stores the details of the assignments given to the students along with the marks obtained by the students for the assignments. Every assignment record is related to a particular student. Internal Marks This entity stores the marks obtained by the students for various subjects. Seminar Evaluations This entity stores the marks obtained by the students for the presentation of the seminars. 2. USE CASE DIAGRAM : The Use Case Diagram shows how different users interact with each other. It helps to understand the application of the application and how each user plays their role. There are three main users of this application. They are as follows: Administrator Faculty Member Student The role of the Administrator includes managing users, setting system settings, and monitoring academic data. The Faculty Member can perform tasks such as recording attendance, updating internal marks, managing assignments, and conducting seminars. The role of the Student includes logging into the application to access their attendance, internal marks, and academic progress. The use case diagram shows how each user can interact with each other. 3. IMPLEMENTATION : The Digital Department Logbook & Academic Management System is based on the MERN stack technology. The MERN stack technology offers a new framework for creating efficient and scalable web applications. The frontend of the Digital Department Logbook & Academic Management System is based on React.js, which offers an interactive and efficient user interface for the system. React components are used to create various dashboards and forms, enabling users to interact with the system easily. The backend of the Digital Department Logbook & Academic Management System is based on Node.js and Express.js, which offer efficient and scalable backend operations for the system. These operations include processing user requests, authenticating users, and interacting with the database. The system is based on the MongoDB database, which stores all the academic records, such as student data, attendance, assignment marks, and seminar evaluations. The system offers efficient and secure authentication options for users. JSON Web Tokens (JWT) are used to verify the users during the login session. Passwords are encrypted using bcrypt and stored in the database. All the above-mentioned technologies have been used to create a reliable and efficient system for managing the department’s academic records. 4. RESULTS AND DISCUSSION : After the implementation of the Digital Department Logbook & Academic Management System, some changes have been observed in the academic record management process. For instance, the faculty members can now record the attendance and marks of the students digitally without the need for maintaining a record. The data will be automatically recorded in the database, thus eliminating the chances of errors. Students can now view the academic performance using the student dashboard. This increases the level of transparency for the students, as they can now keep track of the performance throughout the semester.Using the database, the academic record management has been made easier for the administrators. 5. ADVANTAGES OF THE SYSTEM : * It minimizes manual records * It increases accuracy * It offers a centralized data management system * It increases transparency for students * It saves time for faculty members 6. LIMITATIONS : Drawbacks of using this system: * It requires an internet connection * It may require technical support during installation * Users must be trained to use the system 7. FUTURE ENHANCEMENTS : Possible improvements that can be added to this system in the future include support for a mobile application, integration of biometric devices with attendance, AI-based analysis of performances, and notification for students. 8. REFERENCE : 1. Mokhtar, M. N., Suhaini, S. A., Abdullah, F. H., et al. (2024). A survey of anaesthetic training logbook management among postgraduate students. BMC Medical Education, 24(867). 2. Shafiq, D. A., Marjani, M., Habeeb, R. A., & Asirvatham, D. (2025). Digital Footprints of Academic Success: An Empirical Analysis of Moodle Logs and Traditional Factors for Student Performance. Education Sciences, 15(3), 304. 3. Mustafa, R., & Mustafa, K. (2025). Student Records Management System using IoT. International Journal of Computational and Experimental Science and Engineering. 4. Lontaan, R. J., & Sinadia, A. R. (2024). Design and Development of a Web-Based School Information System. CogITo Smart Journal, 10(2), 593606. 5. Makkaraka, A. M. R. B., Iskandar, A., & Wang, Y. (2024). Design of Web-Based Student Academic Information System. Ceddi Journal of Education, 3(2), 915. 6. Sanchez, L., Penarreta, J., & Soria Poma, X. (2024). Learning Management Systems for Higher Education: A Comparative Study. Discover Education Journal. 7. Kerimbayev, N., Adamova, K., & Shadiev, R. (2025). Intelligent Educational Technologies in Individual Learning: A Systematic Literature Review. Smart Learning Environments Journal. 8. OECD. (2023). OECD Digital Education Outlook 2023: Towards an Effective Digital Education Ecosystem. OECD Publishing. 9. Purnomo, E. N., Imron, A., Wiyono, B. B., & Sobri, A. Y. (2024). Transformation of Digital-Based School Culture and Virtual Learning Environment Integration. Cogent Education Journal. 10. Oxyandi, M., Panduragan, S. L., Said, F. M., & Saputra, M. A. (2023). Use of Electronic Logbook Based on Mobile Learning in Clinical Learning among Students. American Journal of Medical Science and Innovation. 9. CONCLUSION: The Digital Class Log Book System offers an efficient and modern alternative for academic record management for academic institutions. The system replaces the conventional logbook with a digital version that allows lecturers to record daily activities for their classes, attendance, assignments, and internal assessment records in an organized manner. With the implementation of the system, the academic records of the students can be managed securely by the lecturers and the academic institutions. Lecturers can manage the subject records of the students, while the students can view their academic records through the dashboard provided by the system. ______________

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Digital Department Logbook & Academic Management System **DOI :****10.17577/IJERTV15IS030350** Download Full-Text PDF Cite this Publication Jeevanantham G, Amrutha S, Aswini R, Ramya S, Visakh P J, 6 Yusvaanth A S, 2026, Digital Department Logbook & Academic Management System, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 15, Issue 03 , March – 2026 * **Open Access** * Article Download / Views: 0 * **Authors :** Jeevanantham G, Amrutha S, Aswini R, Ramya S, Visakh P J, 6 Yusvaanth A S * **Paper ID :** IJERTV15IS030350 * **Volume & Issue : ** Volume 15, Issue 03 , March – 2026 * **Published (First Online):** 14-03-2026 * **ISSN (Online) :** 2278-0181 * **Publisher Name :** IJERT * **License:** This work is licensed under a Creative Commons Attribution 4.0 International License __ PDF Version View __ Text Only Version #### Digital Department Logbook & Academic Management System (1)Jeevanantham G, (2) Amrutha S, (3) Aswini R, (4) Ramya S, (5) Visakh P J, (6) Yusvaanth A S (1)Assistant Professor (Senior Grade), (12345)Department of Computer Science and Engineering (12345) Nehru Institute of Engineering and Technology Coimbatore -641105 Abstract : Educational institutions handle a huge amount of academic data on a regular basis. These include students’ attendance, internal marks, assignments, evaluation of seminars, and regular academic activities. Most of these academic activities are handled manually, either by maintaining a paper log or by using spreadsheet software. Handling academic records manually often causes problems like computational mistakes, redundancy of data, problems in retrieving data, and lack of transparency for students. Faculty members often spend more time managing records instead of devoting their valuable time to teaching and academic improvements. To overcome these problems, this project aims to implement a Digital Department Logbook & Academic Management System (DDLAMS). DDLAMS is a centralized academic management system that helps manage academic records efficiently within the academic department. Faculty members can easily manage students’ attendance, internal marks, assignments, and regular academic activities through an online interface. Students can also access their records. The system is built using the MERN stack, which includes MongoDB, Express.js, React.js, and Node.js. These technologies enable the system to offer a user interface, security, and effective management of data. Role-Based Access Control (RBAC) is used to offer different access levels for administrators, faculty members, and students. JWT and bcrypt are security techniques used for authenticating and encrypting user data. The implementation of this system ensures transparency, reduces administrative burden, and ensures effective management of academic records. It contributes to the digital transformation of educational institutions by replacing traditional methods with an effective and secure web-based system. Keywords : Academic Management System, Digital Logbook, MERN Stack, Attendance Management, Student Record Management, Role-Based Access Control 1. INTRODUCTION : Educational institutions play an important role in maintaining and managing the academic records of the students. Academic records include student attendance, internal marks, assignment marks, seminar marks, and daily class activities. Managing academic records is important for monitoring the performance of the students and for easy management of academic institutions. For many academic departments, records are managed using traditional methods such as maintaining registers and using spreadsheet files. Although this has been followed for many years, it has many disadvantages. For example, manual records may lead to many human errors, and performing many calculations for attendance and marks may be time- consuming. Managing many registers may not be useful for maintaining and accessing old academic records. Another important factor is the lack of transparency for the students. For example, the attendance and internal marks are not available for the students unless they are announced. This may lead to many confusions and misconceptions about the performance of the students. However, with the advancement of web technologies and digital systems, educational institutions can now use modern technologies to efficiently manage academic records. A digital academic management system can efficiently manage all the data of the departments, as well as automate various processes such as calculating attendance and evaluating marks. A Digital Department Logbook & Academic Management System (DDLAMS) can be used as a solution for the academic departments of educational institutions. The digital logbook will allow the faculty members to efficiently manage the academic records of the departments through a web-based platform. Students can also view their academic records through a digital platform. 2. PROBLEM STATEMENT : Academic departments deal with a lot of information regarding student activities and performance. Still, the traditional methods are used for maintaining these records. There are certain issues with the traditional method of maintaining academic records. The faculty members have to maintain the records of student attendance and marks manually in registers. The calculation of attendance percentages and internal marks for every student is time-consuming and increases the chances of errors. There is also a problem of storage. The academic records are not centrally stored; instead, the information is stored in different registers or files. This creates a problem when the information is required for some purpose. For instance, during academic audits or reviews in the academic departments, the collection of information from the registers or files takes a lot of time. Students are also facing certain issues as the information is not provided to them in a transparent manner. The student does not have the accessibility to the academic records. Therefore, there is a need for a digital solution that can assist in the automation of academic records. 3. OBJECTIVES OF THE STUDY : The main objective of the Digital Department Logbook & Academic Management System is to create an efficient digital platform for the management of academic information. The specific objectives are as follows: * Digitize department logbook records * Reduce paperwork and administration * Provide central storage of academic information * Improve accuracy in attendance and mark calculations * Increase transparency for students * Provide secure access through authentication * Simplify academic reporting and information retrieval By achieving these objectives, it is possible to significantly enhance the efficiency of departmental academic management. 4. LITERATURE SURVEY : Several research works and systems have been developed to enhance academic records management in learning institutions. The Student Information System (SIS) is widely used for storing student information, including personal information, course registration, and results. However, these systems have been mainly used for administrative purposes. Learning Management Systems (LMS) like Moodle and Google Classroom have also been widely used for academic records management. These systems enable learning institutions to conduct online learning activities. They allow tutors to upload assignments, track student performance, and monitor their progress. However, these systems have mainly been used for online learning. Some learning institutions have also used automated attendance management systems that make use of RFID cards, biometric identification, or QR codes. However, these systems have mainly been used for attendance management. Several research works have also been conducted on academic records management, suggesting that integrating different academic functions into one digital platform can enhance efficiency in academic records management, thus reducing data duplication. The Digital Department Logbook & Academic Management System is a platform that combines different academic functions, including attendance managemnt, academic evaluation, and departmental records management, into one platform. 5. EXISTING SYSTEM : In the existing system, the academic records are maintained manually using a paper register or a spreadsheet file. The faculty members record the attendances during each class session and calculate the percentage of attendances manually. Internal assessment marks and assignment scores are also maintained manually. This calculation is often repeated many times, which may cause errors. Another drawback in the existing system is that the data is not stored centrally. Each faculty member may be maintaining the data separately, which may be a problem when the data is required. 6. PROPOSED SYSTEM : The proposed Digital Department Logbook & Academic Management System seeks to address the limitations of the current manual system. The proposed system allows faculty members to record student attendance, update marks, and maintain academic records electronically using a web-based platform. The data collected will be stored in a centralized database. Students can use the proposed system to view their attendance percentage, internal marks, and academic performance. The proposed system ensures that the student has read-only privileges. This proposed system is more efficient and ensures the accuracy of academic record management. 7. SYSTEM ARCHITECTURE : The Digital Department Logbook & Academic Management System (DDLAMS) is developed based on a three-tier architecture model. In this architecture model, there are three layers in which the system is implemented: the Frontend Layer, the Backend Layer, and the Database Layer. Each layer has its own functions, which communicate with other layers for an efficient system. Frontend Layer: The frontend layer is responsible for providing an interface for users. In DDLAMS, the frontend is implemented by using React.js, which is an open-source JavaScript library for building modern web applications. React.js is widely used for developing robust web applications. The frontend is considered the bridge between users and the system. In DDLAMS, different users have different interfaces for different functionalities. Faculty members have different interfaces for different functionalities, such as attendance, internal marks, uploading assignment marks, and daily academic log maintenance. Similarly, different interfaces are provided for different users, such as for students and administrators. Faculty members can maintain attendance records, internal marks, upload their assignment marks, and maintain their daily academic log through the frontend provided by DDLAMS. Similarly, students can log in to the DDLAMS system and view their attendance records, internal marks, and academic log maintained by their faculty members. Additionally, React.js helps in updating the user interface dynamically without reloading the entire page. This improves the performance of the application. Backend Layer : The backend layer is responsible for handling the business logic of the application, which includes processing the data requests received from the frontend layer. In this project, Node.js and Express.js have been used for developing the backend layer. Node.js Node.js is a runtime environment that helps in running JavaScript on the server side. Node.js is known for its high performance, which enables it to handle multiple requests at any given time. Express.js Express.js is a lightweight web development framework that is built on Node.js. Express.js is known for its simplicity in developing web applications. The backend layer has many important tasks to perform, including processing user requests, validating data, authenticating users, and communicating with the database. For example, when a user records their attendance on the frontend layer, the request is sent to the backend server for processing. The request is processed, and the required information is retrieved from the database. The processed information is then sent back to the frontend layer. The second important task that is performed by the backend layer is related to user authentication and authorization. The verification of users identity is performed by this system before allowing them to access the features provided by the application. Database Layer : The database layer is responsible for storing all the academic information that is used by the system. In this project, MongoDB is used as the database management system. MongoDB is a NoSQL database management system that stores information in document form. In this project, academic information is stored in document form, which is very flexible for storing large amounts of information in an efficient manner. The academic information stored in the database includes information about students, staff members, attendance records, marks for assignments, evaluation of seminars, daily academic information, etc. Each type of academic information is stored separately in the database. In addition, MongoDB also supports the efficient retrieval of information, which is very beneficial for displaying academic information to users of the system. The records of all the activities performed in each department of the university can be stored securely in the central database, which can be retrieved when needed. 8. SYSTEM DESIGN : System design is another significant step in the development of the Digital Department Logbook & Academic Management System (DDLAMS). The main aim of the system design is to create an efficient system so that the Digital Department Logbook & Academic Management System can run efficiently and meet the needs of the users. The main aim of the system design is to create a user- friendly interface and a database system for the users. The Digital Department Logbook & Academic Management System is designed for multiple users, including the administrator, faculty members, and students. The users have different roles and permissions to use the system. To allow the users to use the system efficiently, the Digital Department Logbook & Academic Management System provides a separate dashboard for each type of user. The administrator dashboard allows the administrator to manage the users of the system, add or remove users from the system, and manage the settings of the system. The administrator also ensures that the academic records are maintained efficiently within the system. The faculty dashboard has been designed in such a manner that teachers can easily carry out their academic tasks. Using this dashboard, teachers can easily record attendance, internal marks, assignment marks, and daily academic logs. For this purpose, simple forms and input fields have been provided, ensuring accurate input of information. The student dashboard has been designed for the purpose of providing students with accurate information regarding their academic performance. Using this dashboard, students can easily keep track of their attendance percentages, internal marks, assignment marks, and seminar marks. This will help create transparency and provide accurate information to the students. While designing this system, it was ensured that there was a smooth flow of information between the frontend, backend, and database layers. Information provided by teachers is stored in the database and then presented to the students using the user interface. 9. METHODOLOGY : The methodology for the development of the Digital Department Logbook & Academic Management System is a structured approach to ensure the system meets the requirements of the users and runs effectively. The methodology for the development of the system involves a number of steps. Requirement Analysis The first step in the development of the system is the requirement analysis. In this step, the problems associated with the existing system are analyzed.Faculty members find it difficult to maintain registers and calculate attendance or internal marks. Similarly, students do not have easy access to their academic records. Based on the problems of the existing system, the requirements of the digital academic management system are analyzed. System Design After the analysis of the requirements of the digital academic management system, the next step is the system design. This phase of the system design focuses on the architecture of the system. The architecture of the system is designed to meet the requirements of the users. The design of the system focuses on the database of the system. Development This is the stage where the system will be implemented using the various technologies of the MERN stack. The front-end of the system will be built using React.js, while the back-end will be built using Node.js and Express.js. The data storage of the academic information will be handled by MongoDB. Testing This stage is used to test the functions of the system to ensure they are working properly. This is the stage where errors and bugs are found and corrected. The testing of the system will be used to ensure the system will be able to record attendance, store academic information, and control user access. Deployment This is the stage where the system will be deployed to the department. The faculty members of the department will be able to access the system through a web browser. This is the stage where the system will be ready for the real world. 10. SYSTEM MODULES : The Digital Department Logbook & Academic Management System has various modules to enable each user to perform a particular function effectively. This helps in the organization of the system. Staff Module : The Staff Module is for the faculty members of the college. Faculty members are responsible for the management of the academic records. This module helps the teachers to maintain the student records effectively. Functions of the Staff Module: Recording the attendance of the students Recording the internal assessment marks of the students Uploading the marks of the assignments Evaluating the seminar presentations of the students Maintaining the class logs This module helps the teachers to maintain the academic records of the students. The teachers do not have to maintain the records in a physical logbook. Student Module : This module allows students to access their academic records using a login ID. The students have read access to the information, meaning they can only read the information but cannot edit it. Students can read: Attendance percentage Internal assessment marks Assignment results Seminar evaluation scores This module increases transparency for the students. 11. DATABASE DESIGN : The role of database design in the efficient storage and management of academic data cannot be overstated. For this project, MongoDB is used as the database management system. MongoDB MongoDB is a database management system that belongs to the category of NoSQL databases. This means that the data is stored in a document format. Database Design There are a number of collections in the database, each used to store different data. Users Collection In this collection, the login information of the administrator, faculty, and students is stored. Students Collection In this collection, the information of the students, including student ID, name, and department, is stored. Attendance Collection In this collection, the daily attendance information of the students is stored. Assignments Collection This collection holds information about the assignments and the marks for each student. Internal Marks Collection This collection holds information about the internal assessment data for each student. Seminar Evaluations Collection This collection holds the marks for the student seminar evaluations. 1. ER DIAGRAM EXPLANATION : Entity Relationship Diagram The Entity Relationship (ER) Diagram shows the structure of the database and the relationships among various entities of the system. The major entities of the system include Student, Staff, Attendance, Assignments, Internal Marks, and Seminar Evaluations. Student The Student entity includes various attributes like student ID, name, department, etc. Every student has his/her attendance, assignment marks, and internal marks. Staff Staff members are the faculty of the college. They handle the attendance of the students, the assignment marks of the students, etc. Attendance This entity stores the attendance of the students on a daily basis. This entity is related to the Student entity through the student ID. Assignments This entity stores the details of the assignments given to the students along with the marks obtained by the students for the assignments. Every assignment record is related to a particular student. Internal Marks This entity stores the marks obtained by the students for various subjects. Seminar Evaluations This entity stores the marks obtained by the students for the presentation of the seminars. 2. USE CASE DIAGRAM : The Use Case Diagram shows how different users interact with each other. It helps to understand the application of the application and how each user plays their role. There are three main users of this application. They are as follows: Administrator Faculty Member Student The role of the Administrator includes managing users, setting system settings, and monitoring academic data. The Faculty Member can perform tasks such as recording attendance, updating internal marks, managing assignments, and conducting seminars. The role of the Student includes logging into the application to access their attendance, internal marks, and academic progress. The use case diagram shows how each user can interact with each other. 3. IMPLEMENTATION : The Digital Department Logbook & Academic Management System is based on the MERN stack technology. The MERN stack technology offers a new framework for creating efficient and scalable web applications. The frontend of the Digital Department Logbook & Academic Management System is based on React.js, which offers an interactive and efficient user interface for the system. React components are used to create various dashboards and forms, enabling users to interact with the system easily. The backend of the Digital Department Logbook & Academic Management System is based on Node.js and Express.js, which offer efficient and scalable backend operations for the system. These operations include processing user requests, authenticating users, and interacting with the database. The system is based on the MongoDB database, which stores all the academic records, such as student data, attendance, assignment marks, and seminar evaluations. The system offers efficient and secure authentication options for users. JSON Web Tokens (JWT) are used to verify the users during the login session. Passwords are encrypted using bcrypt and stored in the database. All the above-mentioned technologies have been used to create a reliable and efficient system for managing the department’s academic records. 4. RESULTS AND DISCUSSION : After the implementation of the Digital Department Logbook & Academic Management System, some changes have been observed in the academic record management process. For instance, the faculty members can now record the attendance and marks of the students digitally without the need for maintaining a record. The data will be automatically recorded in the database, thus eliminating the chances of errors. Students can now view the academic performance using the student dashboard. This increases the level of transparency for the students, as they can now keep track of the performance throughout the semester.Using the database, the academic record management has been made easier for the administrators. 5. ADVANTAGES OF THE SYSTEM : * It minimizes manual records * It increases accuracy * It offers a centralized data management system * It increases transparency for students * It saves time for faculty members 6. LIMITATIONS : Drawbacks of using this system: * It requires an internet connection * It may require technical support during installation * Users must be trained to use the system 7. FUTURE ENHANCEMENTS : Possible improvements that can be added to this system in the future include support for a mobile application, integration of biometric devices with attendance, AI-based analysis of performances, and notification for students. 8. REFERENCE : 1. Mokhtar, M. N., Suhaini, S. A., Abdullah, F. H., et al. (2024). A survey of anaesthetic training logbook management among postgraduate students. BMC Medical Education, 24(867). 2. Shafiq, D. A., Marjani, M., Habeeb, R. A., & Asirvatham, D. (2025). Digital Footprints of Academic Success: An Empirical Analysis of Moodle Logs and Traditional Factors for Student Performance. Education Sciences, 15(3), 304. 3. Mustafa, R., & Mustafa, K. (2025). Student Records Management System using IoT. International Journal of Computational and Experimental Science and Engineering. 4. Lontaan, R. J., & Sinadia, A. R. (2024). Design and Development of a Web-Based School Information System. CogITo Smart Journal, 10(2), 593606. 5. Makkaraka, A. M. R. B., Iskandar, A., & Wang, Y. (2024). Design of Web-Based Student Academic Information System. Ceddi Journal of Education, 3(2), 915. 6. Sanchez, L., Penarreta, J., & Soria Poma, X. (2024). Learning Management Systems for Higher Education: A Comparative Study. Discover Education Journal. 7. Kerimbayev, N., Adamova, K., & Shadiev, R. (2025). Intelligent Educational Technologies in Individual Learning: A Systematic Literature Review. Smart Learning Environments Journal. 8. OECD. (2023). OECD Digital Education Outlook 2023: Towards an Effective Digital Education Ecosystem. OECD Publishing. 9. Purnomo, E. N., Imron, A., Wiyono, B. B., & Sobri, A. Y. (2024). Transformation of Digital-Based School Culture and Virtual Learning Environment Integration. Cogent Education Journal. 10. Oxyandi, M., Panduragan, S. L., Said, F. M., & Saputra, M. A. (2023). Use of Electronic Logbook Based on Mobile Learning in Clinical Learning among Students. American Journal of Medical Science and Innovation. 9. CONCLUSION: The Digital Class Log Book System offers an efficient and modern alternative for academic record management for academic institutions. The system replaces the conventional logbook with a digital version that allows lecturers to record daily activities for their classes, attendance, assignments, and internal assessment records in an organized manner. With the implementation of the system, the academic records of the students can be managed securely by the lecturers and the academic institutions. Lecturers can manage the subject records of the students, while the students can view their academic records through the dashboard provided by the system. ______________

Digital Department Logbook & Academic Management System View Abstract & download full text of Digital Department Logbook & Academic Management System Download Full-Text PDF Cite this Pu...

#Volume #15, #Issue #03 #(March #2026)

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WLFI price chart

WLFI price chart

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TRUMP price chart

TRUMP price chart

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BCH price chart

BCH price chart

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