Online Book Store Management System: Revolutionizing Literature Retail in the Digital Age
Abstract
Online Book Store Management System approaches literature retail differently. The internet landscape has changed how we access information and make purchases, but this system shows how it can improve the bookshop experience. This online platform lets customers browse, buy, and access a large book collection from home or their phones. Its easy-to-use interface lets customers browse a wide range of genres, authors, and formats. User-generated reviews and ratings let readers choose and discuss books, creating a vibrant and engaging reading community. This Online Book Store Management System simplifies shopping and supports independent writers and publishers through self-publishing and global distribution. It is a dynamic marketplace that unites book lovers with a wide selection of literary content while promoting sustainable resource use through digital services. This system succeeds because it adapts, responds to user feedback, and evolves to meet changing needs and preferences. It shows how technology enhances the love of books, delivering literature to readers and building camaraderie among bookworms. Finally, the Online Book Store Management System combines history and technology to bring a world of reading to users. It opens doors to exploration, discovery, and community, transforming digital book retail. Our online bookshop combines book magic with internet buying. Enjoy the extraordinary.
References
B. Joshi and S. Parvatikar, "Online book recommendation system by using collaborative filtering and association mining," IEEE International Conference on Computational Intelligence and Computing Research, Almaty, Kazakhstan, 2015.
N. H. Mustaffa, N. F. A. Razak, N. H. M. Radzi, R. Sallehhuddin, E. N. Bazin, "Online food ordering system with short message notification," Int. J. Emerging Res. Management Technol., vol. 4, no. 12, pp. 1-4, 2015.
V. Rajeev P and S. Rekha V, "Recommending products to customers using opinion mining of online product reviews and features," International Conference on Circuit, Power and Computing Technologies [ICCPCT], Coimbatore, Tamil Nadu, India, 2015.
Razeghi, M., Dehzangi, A., Wu, D., McClintock, R., Zhang, Y., Durlin, Q., ... & Meng, F. (2019, May). Antimonite-based gap-engineered type-II superlattice materials grown by MBE and MOCVD for the third generation of infrared imagers. In Infrared Technology and Applications XLV (Vol. 11002, pp. 108-125). SPIE.
Meng, F., Zhang, L., & Chen, Y. (2023) FEDEMB: An Efficient Vertical and Hybrid Federated Learning Algorithm Using Partial Network Embedding.
Meng, F., Jagadeesan, L., & Thottan, M. (2021). Model-based reinforcement learning for service mesh fault resiliency in a web application-level. arXiv preprint arXiv:2110.13621.
B. Senapati and B. S. Rawal, “Adopting a deep learning split-protocol based predictive maintenance management system for industrial manufacturing operations,” in Lecture Notes in Computer Science, Singapore: Springer Nature Singapore, pp. 22–39, 2023.
B. Senapati and B. S. Rawal, “Quantum communication with RLP quantum resistant cryptography in industrial manufacturing,” Cyber Security and Applications, vol. 1, no. 12, p. 100019, 2023.
B. Senapati et al., “Wrist crack classification using deep learning and X-ray imaging,” in Proceedings of the Second International Conference on Advances in Computing Research (ACR’24), Cham: Springer Nature Switzerland, pp. 60–69, 2024.
M. A. Yassin et al., “Advancing SDGs : Predicting Future Shifts in Saudi Arabia ’ s Terrestrial Water Storage Using Multi-Step-Ahead Machine Learning Based on GRACE Data,” 2024.
M. A. Yassin, A. G. Usman, S. I. Abba, D. U. Ozsahin, and I. H. Aljundi, “Intelligent learning algorithms integrated with feature engineering for sustainable groundwater salinization modelling: Eastern Province of Saudi Arabia,” Results Eng., vol. 20, p. 101434, 2023.
S. I. Abba, A. G. Usman, and S. IŞIK, “Simulation for response surface in the HPLC optimization method development using artificial intelligence models: A data-driven approach,” Chemom. Intell. Lab. Syst., vol. 201, no. April, 2020.
A. G. Usman et al., “Environmental modelling of CO concentration using AI-based approach supported with filters feature extraction: A direct and inverse chemometrics-based simulation,” Sustain. Chem. Environ., vol. 2, p. 100011, 2023.
P. P. Anand, U. K. Kanike, P. Paramasivan, S. S. Rajest, R. Regin, and S. S. Priscila, “Embracing Industry 5.0: Pioneering Next-Generation Technology for a Flourishing Human Experience and Societal Advancement,” FMDB Transactions on Sustainable Social Sciences Letters, vol.1, no. 1, pp. 43–55, 2023.
G. Gnanaguru, S. S. Priscila, M. Sakthivanitha, S. Radhakrishnan, S. S. Rajest, and S. Singh, “Thorough analysis of deep learning methods for diagnosis of COVID-19 CT images,” in Advances in Medical Technologies and Clinical Practice, IGI Global, pp. 46–65, 2024.
G. Gowthami and S. S. Priscila, “Tuna swarm optimisation-based feature selection and deep multimodal-sequential-hierarchical progressive network for network intrusion detection approach,” Int. J. Crit. Comput.-based Syst., vol. 10, no. 4, pp. 355–374, 2023.
A. J. Obaid, S. Suman Rajest, S. Silvia Priscila, T. Shynu, and S. A. Ettyem, “Dense convolution neural network for lung cancer classification and staging of the diseases using NSCLC images,” in Proceedings of Data Analytics and Management, Singapore; Singapore: Springer Nature, pp. 361–372, 2023.
S. S. Priscila and A. Jayanthiladevi, “A study on different hybrid deep learning approaches to forecast air pollution concentration of particulate matter,” in 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2023.
S. S. Priscila, S. S. Rajest, R. Regin, and T. Shynu, “Classification of Satellite Photographs Utilizing the K-Nearest Neighbor Algorithm,” Central Asian Journal of Mathematical Theory and Computer Sciences, vol. 4, no. 6, pp. 53–71, 2023.
S. S. Priscila and S. S. Rajest, “An Improvised Virtual Queue Algorithm to Manipulate the Congestion in High-Speed Network”,” Central Asian Journal of Medical and Natural Science, vol. 3, no. 6, pp. 343–360, 2022.
S. S. Priscila, S. S. Rajest, S. N. Tadiboina, R. Regin, and S. András, “Analysis of Machine Learning and Deep Learning Methods for Superstore Sales Prediction,” FMDB Transactions on Sustainable Computer Letters, vol. 1, no. 1, pp. 1–11, 2023.
R. Regin, Shynu, S. R. George, M. Bhattacharya, D. Datta, and S. S. Priscila, “Development of predictive model of diabetic using supervised machine learning classification algorithm of ensemble voting,” Int. J. Bioinform. Res. Appl., vol. 19, no. 3, 2023.
S. Silvia Priscila, S. Rajest, R. Regin, T. Shynu, and R. Steffi, “Classification of Satellite Photographs Utilizing the K-Nearest Neighbor Algorithm,” Central Asian Journal of Mathematical Theory and Computer Sciences, vol. 4, no. 6, pp. 53–71, 2023.
S. S. Rajest, S. Silvia Priscila, R. Regin, T. Shynu, and R. Steffi, “Application of Machine Learning to the Process of Crop Selection Based on Land Dataset,” International Journal on Orange Technologies, vol. 5, no. 6, pp. 91–112, 2023.
S. D. Beedkar, C. N. Khobragade, S. S. Chobe, B. S. Dawane, and O. S. Yemul, "Novel thiazolo-pyrazolyl derivatives as xanthine oxidase inhibitors and free radical scavengers," International Journal of Biological Macromolecules, vol. 50, no. 4, pp. 947-956, 2012.
S. S. Chobe, V. A. Adole, K. P. Deshmukh, T. B. Pawar, and B. S. Jagdale, "Poly (ethylene glycol)(PEG-400): A green approach towards synthesis of novel pyrazolo [3, 4-d] pyrimidin-6-amines derivatives and their antimicrobial screening," Archives of Applied Science Research, vol. 6, no. 2, pp. 61-66, 2014.
S. S. Chobe, B. S. Dawane, K. M. Tumbi, P. P. Nandekar, and A. T. Sangamwar, "An ecofriendly synthesis and DNA binding interaction study of some pyrazolo [1, 5-a] pyrimidines derivatives," Bioorganic & Medicinal Chemistry Letters, vol. 22, no. 24, pp. 7566-7572, 2012.
S. S. Chobe, R. D. Kamble, S. D. Patil, A. P. Acharya, S. V. Hese, O. S. Yemul, and B. S. Dawane, "Green approach towards synthesis of substituted pyrazole-1, 4-dihydro, 9-oxa, 1, 2, 6, 8-tetrazacyclopentano [b] naphthalene-5-one derivatives as antimycobacterial agents," Medicinal Chemistry Research, vol. 22, pp. 5197-5203, 2013.
B. S. Dawane, S. G. Konda, N. T. Khandare, S. S. Chobe, B. M. Shaikh, R. G. Bodade, and V. D. Joshi, "Synthesis and antimicrobial evaluation of 2-(2-butyl-4-chloro-1H-imidazol-5-yl-methylene)-substituted-benzofuran-3-ones," Organic Communications, vol. 3, no. 2, pp. 22, 2010.
B. S. Dawane, S. G. Konda, B. M. Shaikh, S. S. Chobe, N. T. Khandare, V. T. Kamble, and R. B. Bhosale, "Synthesis and in vitro antimicrobial activity of some new 1-thiazolyl-2-pyrazoline derivatives," Synthesis, vol. 1, no. 009, 2010.
B. S. Dawane, B. M. Shaikh, N. T. Khandare, V. T. Kamble, S. S. Chobe, and S. G. Konda, "Eco-friendly polyethylene glycol-400: a rapid and efficient recyclable reaction medium for the synthesis of thiazole derivatives," Green Chemistry Letters and Reviews, vol. 3, no. 3, pp. 205-208, 2010.
M. Haroun, S. S. Chobe, R. R. Alavala, S. M. Mathure, R. N. Jamullamudi, C. K. Nerkar, and M. K. Anwer, "1, 5-benzothiazepine derivatives: green synthesis, in silico and in vitro evaluation as anticancer agents," Molecules, vol. 27, no. 12, pp. 3757, 2022.
S. A. Shaikh, S. R. Labhade, R. R. Kale, P. Y. Pachorkar, R. J. Meshram, K. S. Jain, and D. R. Boraste, "Thiadiazole-Thiazole Derivatives as Potent Anti-Tubercular Agents: Synthesis, Biological Evaluation, and In Silico Docking Studies," European Journal of Medicinal Chemistry Reports, vol. 100183, 2024.
S. A. Shaikh, S. R. Labhade, R. R. Kale, P. Y. Pachorkar, R. J. Meshram, K. S. Jain, and S. N. Wakchaure, "Synthesis, Biological and Molecular Docking Studies of Thiazole‐Thiadiazole derivatives as potential Anti‐Tuberculosis Agents," Chemistry & Biodiversity, vol. 21, no. 6, e202400496, 2024.
Kaluvakuri, V. P. K. (2023). AI-Powered Continuous Deployment: Achieving Zero Downtime and Faster Releases. SSRN Electronic Journal. https://ssrn.com/abstract=4927198 or
Kaluvakuri, V. P. K. (2023). Revolutionizing Fleet Accident Response with AI: Minimizing Downtime, Enhancing Compliance, and Transforming Safety. SSRN Electronic Journal.
Kaluvakuri, V. P. K. (2023). AI-Driven Fleet Financing: Transparent, Flexible, and Upfront Pricing for Smarter Decisions. SSRN Electronic Journal. https://ssrn.com/abstract=4927194 or
Kaluvakuri, V. P. K., Peta, V. P., & Khambam, S. K. R. (2021). Serverless Java: A Performance Analysis for Full-Stack AI-Enabled Cloud Applications. SSRN Electronic Journal. https://ssrn.com/abstract=4927228 or
P. Sonawane, C. L. Ladekar, G. A. Badiger, and R. A. Deore, "Design and analysis of serviceable cantilever fit snap in automotive plastic parts," World Journal of Engineering, vol. ahead-of-print, no. ahead-of-print, 2024.
P. R. Sonawane, D. M. Deshmukh, A. Gajbhiye, et al., "An investigation into the mechanical properties of an epoxy-based composite made from jute fiber and reinforced with Sal tree gum powder," Journal of the Institution of Engineers (India): Series D, vol. 105, pp. 665–674, 2024.
A. M. Gajbhiye, P. R. Sonawane, A. H. Karle, et al., "Optimization of welding parameters for En8D and SAE1018 materials by Taguchi," International Journal on Interactive Design and Manufacturing, 2023.
M. Chandrasekaran, P. R. Sonawane, and P. Sriramya, "Prediction of gear pitting severity by using Naive Bayes machine learning algorithm," in Recent Advances in Materials and Modern Manufacturing, I. A. Palani, P. Sathiya, and D. Palanisamy, Eds. Springer, Singapore, 2022.
P. R. Sonawane, D. M. Deshmukh, V. A. Utikar, S. S. Jadhav, and G. A. Deshpande, "Nanocellulose in metals: Advancing sustainable practices in metal refining and extraction processes," Journal of Mines, Metals and Fuels, vol. 71, no. 12, pp. 2773–2783, 2023.
P. Sonawane and S. Walavalkar, "Design of finger and thumb mechanism for prosthetic arm," International Review of Mechanical Engineering (IREME), vol. 11, no. 7, pp. 460-466, 2017.
U. Gaikwad, P. Sonawane, and R. Pawar, "Photo-elastic investigation of worm gear," International Review of Mechanical Engineering (IREME), vol. 11, no. 6, pp. 393-399, 2017.
S. Temara, “Maximizing Penetration Testing Success with Effective Reconnaissance Techniques Using ChatGPT”, Asian Journal of Research in Computer Science, vol. 17, no. 5, pp. 19–29, 2024.
S. Temara, “The Ransomware Epidemic: Recent Cybersecurity Incidents Demystified”, Asian Journal of Advanced Research and Reports, vol. 18, no. 3, pp. 1–16, Feb. 2024.
S. Temara, “Harnessing the power of artificial intelligence to enhance next-generation cybersecurity,” World Journal of Advanced Research and Reviews, vol. 23, no. 2, pp. 797–811,2024.
D. Dayana, T. S. Shanthi, G. Wali, P. V. Pramila, T. Sumitha, and M. Sudhakar, “Enhancing usability and control in artificial intelligence of things environments (AIoT) through semantic web control models,” in Semantic Web Technologies and Applications in Artificial Intelligence of Things, F. Ortiz-Rodriguez, A. Leyva-Mederos, S. Tiwari, A. Hernandez-Quintana, and J. Martinez-Rodriguez, Eds., IGI Global, USA, 2024, pp. 186–206.
J. Tanwar, H. Sabrol, G. Wali, C. Bulla, R. K. Meenakshi, P. S. Tabeck, and B. Surjeet, “Integrating blockchain and deep learning for enhanced supply chain management in healthcare: A novel approach for Alzheimer’s and Parkinson’s disease prevention and control,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 22s, pp. 524–539, 2024.
R. K. Meenakshi, R. S., G. Wali, C. Bulla, J. Tanwar, M. Rao, and B. Surjeet, “AI integrated approach for enhancing linguistic natural language processing (NLP) models for multilingual sentiment analysis,” Philological Investigations, vol. 23, no. 1, pp. 233–247, 2024.
G. Wali and C. Bulla, “Suspicious activity detection model in bank transactions using deep learning with fog computing infrastructure,” in Advances in Computer Science Research, 2024, pp. 292–302.
G. Wali, P. Sivathapandi, C. Bulla, and P. B. M. Ramakrishna, “Fog computing: Basics, key technologies, open issues, and future research directions,” African Journal of Biomedical Research, vol. 27, no. 9, pp. 748–770, 2024.
Wali, G., and C. Bulla, “Anomaly Detection in Fog Computing: State-of-the-Art Techniques, applications, Challenges, and Future Directions,” Library Progress International, vol. 44, no. 3, pp. 13967–13993, 2024.
Wali, G., and C. Bulla, “A Data Driven Risk Assessment in Fractional Investment in Commercial Real Estate using Deep Learning Model and Fog Computing Infrastructure,” Library Progress International, vol. 44, no. 3, pp. 4128–4141, 2024.
Kaluvakuri, V. P. K., Khambam, S. K. R., & Peta, V. P. (2021). AI-Powered Predictive Thread Deadlock Resolution: An Intelligent System for Early Detection and Prevention of Thread Deadlocks in Cloud Applications. SSRN Electronic Journal.
Kaluvakuri, V. P. K., & Peta, V. P. (2022). Beyond The Spreadsheet: A Machine Learning & Cloud Approach to Streamlined Fleet Operations and Personalized Financial Advice. SSRN Electronic Journal.
Kaluvakuri, V. P. K., Peta, V. P., & Khambam, S. K. R. (2022). Engineering Secure Ai/Ml Systems: Developing Secure Ai/Ml Systems With Cloud Differential Privacy Strategies. SSRN Electronic Journal.
Kaluvakuri, V. P. K., & Peta, V. P. (2023). The Impact of AI and Cloud on Fleet Management and Financial Planning: A Comparative Analysis. SSRN Electronic Journal. https://ssrn.com/abstract=4927212 or
Kaluvakuri, V. P. K., Peta, V. P., & Khambam, S. K. R. (2023). Ai-Driven Root Cause Analysis for Java Memory Leaks. SSRN Electronic Journal. https://ssrn.com/abstract=4927210 or
Kaluvakuri, V. P. K., & Khambam, S. K. R. (2024). Securing Telematics Data in Fleet Management: Integrating IAM with ML Models for Data Integrity in Cloud-Based Applications. SSRN Electronic Journal.
T. Shynu, A. J. Singh, B. Rajest, S. S. Regin, and R. Priscila, “Sustainable intelligent outbreak with self-directed learning system and feature extraction approach in technology,” International Journal of Intelligent Engineering Informatics, vol. 10, no. 6, pp.484-503, 2022.
S. S. Priscila, D. Celin Pappa, M. S. Banu, E. S. Soji, A. T. A. Christus, and V. S. Kumar, “Technological frontier on hybrid deep learning paradigm for global air quality intelligence,” in Cross-Industry AI Applications, IGI Global, pp. 144–162, 2024.
S. S. Priscila, E. S. Soji, N. Hossó, P. Paramasivan, and S. Suman Rajest, “Digital Realms and Mental Health: Examining the Influence of Online Learning Systems on Students,” FMDB Transactions on Sustainable Techno Learning, vol. 1, no. 3, pp. 156–164, 2023.
S. R. S. Steffi, R. Rajest, T. Shynu, and S. S. Priscila, “Analysis of an Interview Based on Emotion Detection Using Convolutional Neural Networks,” Central Asian Journal of Theoretical and Applied Science, vol. 4, no. 6, pp. 78–102, 2023.
A. Gbadamosi et al., “New-generation machine learning models as prediction tools for modeling interfacial tension of hydrogen-brine system,” Int. J. Hydrogen Energy, vol. 50, pp. 1326–1337, 2024.
I. Abdulazeez, S. I. Abba, J. Usman, A. G. Usman, and I. H. Aljundi, “Recovery of Brine Resources Through Crown-Passivated Graphene, Silicene, and Boron Nitride Nanosheets Based on Machine-Learning Structural Predictions,” ACS Appl. Nano Mater., 2023.
B. S. Alotaibi et al., “Sustainable Green Building Awareness: A Case Study of Kano Integrated with a Representative Comparison of Saudi Arabian Green Construction,” Buildings, vol. 13, no. 9, 2023.
S. I. Abba et al., “Integrated Modeling of Hybrid Nanofiltration/Reverse Osmosis Desalination Plant Using Deep Learning-Based Crow Search Optimization Algorithm,” Water (Switzerland), vol. 15, no. 19, 2023.
S. I. Abba, J. Usman, and I. Abdulazeez, “Enhancing Li + recovery in brine mining : integrating next-gen emotional AI and explainable ML to predict adsorption energy in crown ether-based hierarchical nanomaterials,” pp. 15129–15142, 2024.
J. Usman, S. I. Abba, N. Baig, N. Abu-Zahra, S. W. Hasan, and I. H. Aljundi, “Design and Machine Learning Prediction of In Situ Grown PDA-Stabilized MOF (UiO-66-NH2) Membrane for Low-Pressure Separation of Emulsified Oily Wastewater,” ACS Appl. Mater. Interfaces, Mar. 2024.
R. Rai, A. Shrestha, S. Rai, S. Chaudhary, D. K. Acharya, and S. Subedi, “Conversion of farming systems into organic biointensive farming systems and the transition to sustainability in agro-ecology: Pathways towards sustainable agriculture and food systems,” J. Multidiscip. Sci., vol. 6, no. 1, pp. 26–31, Jun. 2024.
S. Rai and R. Rai, “Advancements and practices in budding techniques for kiwifruit propagation,” J. Multidiscip. Sci., vol. 6, no. 1, pp. 26–31, Jun. 2024.
S. Rai and R. Rai, “Monkey menace in Nepal: An analysis and proposed solutions,” J. Multidiscip. Sci., vol. 6, no. 1, pp. 26–31, Jun. 2024.
K. Shrestha, S. Chaudhary, S. Subedi, S. Rai, D. K. Acharya, and R. Rai, “Farming systems research in Nepal: Concepts, design, and methodology for enhancing agricultural productivity and sustainability,” J. Multidiscip. Sci., vol. 6, no. 1, pp. 17–25, May 2024.
S. Rai and R. Rai, “Advancement of kiwifruit cultivation in Nepal: Top working techniques,” J. Multidiscip. Sci., vol. 6, no. 1, pp. 11–16, Feb. 2024.
S. Chaudhary, A. K. Shrestha, S. Rai, D. K. Acharya, S. Subedi, and R. Rai, “Agroecology integrates science, practice, movement, and future food systems,” J. Multidiscip. Sci., vol. 5, no. 2, pp. 39–60, Dec. 2023.
R. Rai, V. Y. Nguyen, and J. H. Kim, “Variability analysis and evaluation for major cut flower traits of F1 hybrids in Lilium brownii var. colchesteri,” J. Multidiscip. Sci., vol. 4, no. 2, pp. 35–41, Dec. 2022
V. Y. Nguyen, R. Rai, J.-H. Kim, J. Kim, and J.-K. Na, “Ecogeographical variations of the vegetative and floral traits of Lilium amabile Palibian,” J. Plant Biotechnol., vol. 48, no. 4, pp. 236–245, Dec. 2021.
R. Rai and J. H. Kim, “Performance evaluation and variability analysis for major growth and flowering traits of Lilium longiflorum Thunb. genotypes,” J. Exp. Biol. Agric. Sci., vol. 9, no. 4, pp. 439–444, Aug. 2021.
S. Banala, “The Future of IT Operations: Harnessing Cloud Automation for Enhanced Efficiency and The Role of Generative AI Operational Excellence,” International Journal of Machine Learning and Artificial Intelligence, vol. 5, no. 5, pp. 1–15, Jul. 2024.
S. Banala, "DevOps Essentials: Key Practices for Continuous Integration and Continuous Delivery," International Numeric Journal of Machine Learning and Robots, vol. 8, no. 8, pp. 1-14, 2024.
M. R. M. Reethu, L. N. R. Mudunuri, and S. Banala, “Exploring the Big Five Personality Traits of Employees in Corporates,” FMDB Transactions on Sustainable Management Letters, vol. 2, no. 1, pp. 1–13, 2024.
S. Banala, “The Future of Site Reliability: Integrating Generative AI into SRE Practices,” FMDB Transactions on Sustainable Computer Letters, vol. 2, no. 1, pp. 14–25, 2024.
S. Banala, Identity and Access Management in the Cloud, International Journal of Innovations in Applied Sciences & Engineering, vol. 10, no. 1S, pp. 60–69, 2024.
S. Banala, "The FinOps Framework: Integrating Finance and Operations in the Cloud," International Journal of Advances in Engineering Research, vol. 26, no. 6, pp. 11–23, 2024.
S. Banala, "Artificial Creativity and Pioneering Intelligence: Harnessing Generative AI to Transform Cloud Operations and Environments," International Journal of Innovations in Applied Sciences and Engineering, vol. 8, no. 1, pp. 34–40, 2023.
S. Banala, Cloud Sentry: Innovations in Advanced Threat Detection for Comprehensive Cloud Security Management, International Journal of Innovations in Scientific Engineering, vol. 17, no. 1, pp. 24–35, 2023.
S. Banala, Exploring the Cloudscape - A Comprehensive Roadmap for Transforming IT Infrastructure from On-Premises to Cloud-Based Solutions, International Journal of Universal Science and Engineering, vol. 8, no. 1, pp. 35–44, 2022.
S. K. Suvvari, "An exploration of agile scaling frameworks: Scaled agile framework (SAFe), large-scale scrum (LeSS), and disciplined agile delivery (DAD)," Int. J. Recent Innov. Trends Comput. Commun., vol. 7, no. 12, pp. 9–17, 2019.
S. K. Suvvari, B. Anjum, and M. Hussain, "Key factors impacting the e-learning effectiveness for computer science students: An empirical study," Webology, vol. 17, no. 4, pp. 837–847, 2020.
Ali, M. Ahmad, S. Nawaz, T. Raza, and S. K. Suvvari, "An effective structure for data management in the cloud-based tools and techniques," J. Eng. Sci., vol. 15, no. 4, pp. 215–228, 2022.
A. B. Naeem et al., “Heart disease detection using feature extraction and artificial neural networks: A sensor-based approach,” IEEE Access, vol. 12, no.3, pp. 37349–37362, 2024.
R. Tsarev et al., “Automatic generation of an algebraic expression for a Boolean function in the basis ∧, ∨, ¬,” in Data Analytics in System Engineering, Cham: Springer International Publishing, Switzerland, pp. 128–136, 2024.
R. Tsarev, B. Senapati, S. H. Alshahrani, A. Mirzagitova, S. Irgasheva, and J. Ascencio, “Evaluating the effectiveness of flipped classrooms using linear regression,” in Data Analytics in System Engineering, Cham: Springer International Publishing, Switzerland, pp. 418–427, 2024.
Meng, F., Zhang, L., Chen, Y., & Wang, Y. (2023). Sample-based Dynamic Hierarchical Transformer with Layer and Head Flexibility via Contextual Bandit. Authorea Preprints.
7.png)
3.png)
