Volume-13 , Issue-1 , Jan -2025, E-ISSN: 2347-2693

A major step forward in guaranteeing data integrity, into safe data engineering processes. The immutability and decentralization of blockchain ledgers make it an ideal solution for problems including data provenance, access control, and tamper resistance. Examining blockchain`s potential in safe data processes, this research highlights the technology`s ability to facilitate real-time data exchange, strengthen audit trails, and improve compliance with regulatory requirements. Some important use cases include distributed system data sharing in a safe environment, smart contract-based simplified access control, and immutable tracking of data modifications. New approaches, such as hybrid blockchain models and layer-two scaling methodologies, are being considered as potential answers to existing problems, including scalability, integration complexity, and energy efficiency. The results show that blockchain technology, when used correctly, may make data processes more trustworthy and resilient, giving businesses an advantage when it comes to handling important and sensitive data. To highlight blockchain`s revolutionary potential in safe data ecosystems, this article finishes with suggestions for applying blockchain-based solutions to data engineering techniques. Blockchain technology offers a fresh perspective on data quality, security, and transparency issues when integrated into safe data engineering procedures. The distributed and immutable ledger technology known as blockchain provides a solid basis for building confidence in data-driven procedures. Data provenance, safe sharing, and auditability are three important topics that this study focusses on as it analyses the potential of blockchain in improving secure data operations. Blockchain technology allows distributed systems to have automatic validation and safe interactions by using smart contracts and cryptographic approaches. According to the results, using blockchain technology improves data security and boosts operational efficiency by cutting out middlemen. But there are obstacles that must be carefully considered, including compatibility, adoption costs, and scalability. The paper finishes with some suggestions for how data engineering processes might make the most of blockchain technology, which has the ability to revolutionize data management methods and guarantee compliance and security in contemporary ecosystems.

Data Auditability, Block chain Technology, Secure Data Engineering, Data Provenance, Smart Contracts, Data Integrity.

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[8] P. Zhang and M. Zhou, "Security and trust in blockchains: Architecture, key technologies, and open issues," IEEE Transactions on Computational Social Systems, Vol.7, No.3, pp.790–801, 2020.
[9] Z. Peng, H. Wu, B. Xiao, and S. Guo, "VQL: Providing query efficiency and data authenticity in blockchain systems," in 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), Apr., pp.1–6, 2019.
[10] V. Clincy and H. Shahriar, "Blockchain development platform comparison," in 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), Jul., Vol.1, pp.922–923, 2019.

Chittaranjan Pradhan, Abhishek Trehan, "Integration of Blockchain Technology in Secure Data Engineering Workflows," International Journal of Computer Sciences and Engineering, Vol.13, Issue.1, pp.1-7, 2025.

The new area on prompt engineering throughout natural language processing (NLP) is investigated during the current article. It look at different approaches and strategies for creating prompts that maximize the functionality of big language models like GPT-4. The study outlines the importance of fast engineering in enhancing model outputs, talks about the difficulties encountered, and provides example studies showing effective implementations in various fields. NLP has evolved dramatically with the introduction on large language models (LLMs) similar GPT-4, which allow machines to produce text that is remarkably coherent and fluent, much like that of a human. However, the prompts that these models are given have a significant impact on how effective they are. The technique of creating and improving prompts to improve model performance, known as prompt engineering, has become a crucial field of study. This essay offers a thorough analysis regarding rapid engineering, looking at its methods, theoretical underpinnings, along with real-world applications. We begin by defining prompt engineering and contextualizing its importance within the broader landscape of NLP and AI. A thorough review of existing literature reveals various techniques and strategies for constructing effective prompts, including template-based approaches, prompt tuning, and the use of prompt-based transfer learning. The paper also addresses the challenges inherent in prompt engineering, such as managing ambiguity, mitigating bias, and ensuring scalability across different applications. Through detailed case studies, we illustrate the impact of prompt engineering on diverse domains, including education, healthcare, business, and creative industries. These examples demonstrate how tailored prompts will significantly boost the model outputs` quality and relevance, improving user experiences while streamlining workflows. Finally, the paper discusses future directions in prompt engineering research, highlighting the potential for automated prompt generation, integration with other AI technologies, and interdisciplinary applications. We can open up new avenues for AI-driven innovative thinking and problem-solving by improving our comprehension of and utilization of prompt engineering. This study emphasizes how important quick engineering can be for maximizing LLM capabilities advocating for continued investment in this field to address current challenges and explore new opportunities.

Prompt Engineering, Natural Language Processing, GPT-4, Language Models, AI, Machine Learning

[1] OpenAI, "ChatGPT: A Generative Pre-trained Transformer for Natural Language Processing," International Journal of Artificial Intelligence Research, Vol.10, Issue.2, pp.100-110, 2021.
[2] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, ?., & Polosukhin, I., "Attention Is All You Need," In the Proceedings of the 2017 Advances in Neural Information Processing Systems Conference (NeurIPS), pp.5998-6008, 2017
[3] Diab, M., Herrera, J., & Chernow, B., Stable Diffusion Prompt Book. ISROSET Publisher, India, pp.1-150, 2022
[4] J. Gu et al., “A systematic survey of prompt engineering on vision-language foundation models,”arXiv preprint arXiv:2307.12980, 2023.
[5] DataCamp, Prompt Engineering: A Detailed Guide for 2024.
[6] Hyojin Bahng, Ali Jahanian, Swami Sankaranarayanan, and Phillip Isola. Exploring visual prompts for adapting large-scale models. arXiv preprint arXiv:2203.17274, 2022.
[7] Wenhu Chen, Xueguang Ma, XinyiWang, and William W Cohen. Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks. arXiv preprint arXiv:2211.12588, 2022.
[8] Banghao Chen, Zhaofeng Zhang, Nicolas Langrené, and Shengxin Zhu. Unleashing the potential of prompt engineering in large language models: a comprehensive review. arXiv preprint arXiv:2310.14735, 2023.
[9] Yew Ken Chia, Guizhen Chen, Luu Anh Tuan, Soujanya Poria, and Lidong Bing. Contrastive chainof-thought prompting. arXiv preprint arXiv:2311.09277, 2023.
[10] Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston. Chain-of-verification reduces hallucination in large language models. arXiv preprint arXiv:2309.11495, 2023.
[11] Shizhe Diao, Pengcheng Wang, Yong Lin, and Tong Zhang. Active prompting with chainof-thought for large language models. arXiv preprint arXiv:2302.12246, 2023.
[12] S. Biswas, Prospective Role of Chat GPT in the Military: According to ChatGPT (Qeios), 2023.
[13] R.W. McGee, “Who Were the 10 Best and 10 Worst US Presidents? The Opinion of ChatGPT (Artificial Intelligence),” Opin. ChatGPT (Artif. Intell.), February 23, 2023.
[14] C. Wu, S. Yin, W. Qi, X. Wang, Z. Tang, N. Duan, “Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models,” arXiv preprint, arXiv:2303.04671, 2023
[15] D. Baidoo-Anu, L. Owusu Ansah, Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning, 2023.
[16] A. Howard, W. Hope, A. Gerada, ChatGPT and antimicrobial advice: the end of the consulting infection doctor? Lancet Infect. Dis., 2023.
[17] T.Y. Zhuo, Y. Huang, C. Chen, Z. Xing, Exploring Ai Ethics of Chatgpt: A Diagnostic Analysis, arXiv preprint arXiv:2301.12867, 2023.
[18] E. Kasneci, K. Seßler, S. Küchemann, M. Bannert, D. Dementieva, F. Fischer, U. Gasser, G. Groh, S. Günnemann, E. Hüllermeier, S. Krusche, ChatGPT for good? On opportunities and challenges of large language models for education, Learn. Indiv Differ 103, 102274, 2023.
[19] X. Zheng, C. Zhang, P.C. Woodland, Adapting GPT, GPT-2 and BERT language models for speech recognition, in: 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), IEEE, December, pp.162–168, 2021.
[20] S. Liu, X. Huang, A Chinese question answering system based on gpt, in: 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS), IEEE, October, pp.533–537, 2019.
[21] Movement, Q. ai-Powering a P. W., What Is ChatGPT? How AI Is Transforming Multiple Industries. Forbes, 2023.

Jatin Kumar Panjavani, "Unlocking Potential: Advancements and Applications in Prompt Engineering for NLP," International Journal of Computer Sciences and Engineering, Vol.13, Issue.1, pp.8-16, 2025.

Improved user experiences and data-driven solutions have resulted from the revolutionary combination of Machine Learning (ML) with Fullstack Development, which has changed the way intelligent apps are produced. Using Microsoft`s flexible ML.NET framework, this article delves into how to incorporate Machine Learning models into Fullstack Development without a hitch. Without deep knowledge of data science, ML.NET allows developers to build, train, and deploy ML models inside.NET apps. At the outset, the research delves into the difficulties encountered by conventional full-stack apps, including their lack of predictive power and static data processing. Fullstack designs that use ML.NET allow applications to automate decision-making, tailor content, and evaluate and forecast user actions in real-time. We showcase ML.NET`s interoperability with common programming languages like C# and F#, its support for various data types, and automated machine learning (AutoML) to show how it can be used in fullstack applications. Model building, training, and deployment are the three main areas covered in the methodology part as they pertain to developers utilizing ML.NET in a fullstack setting. To facilitate effective data processing and model inference, the focus is on connecting backend systems with ML.NET pipelines. Our research also delves into frontend integration strategies, showing how features driven by ML may improve user interfaces with capabilities like natural language processing, visual analytics, and real-time suggestions. To show how ML.NET may be used in fullstack development, many example studies are given. The development of e-commerce platform recommendation systems, company dashboard predictive analytics, and customer feedback management sentiment analysis tools are all examples of what is involved. Each example demonstrates how easy it is to include ML models into preexisting fullstack frameworks like as Angular, React, and ASP.NET Core. In terms of computational efficiency, scalability, and accuracy, the study compares and contrasts the performance of ML.NET models with those of established ML frameworks. We talk about the problems and possible solutions that come up during integration, including dealing with huge datasets, improving the performance of the models, and making sure they work on different platforms. To sum up, ML.NET`s integration with Fullstack Development paves the way for developers to build smart, scalable, user-centric apps. Utilizing ML.NET, developers can connect the dots between classic software engineering and cutting-edge AI, revolutionizing user interfaces and data processing for organizations.

Fullstack Development, Intelligent Applications, ML.NET, Predictive Analytics Machine Learning.

[1] S. Boovaraghavan, A. Maravi, P. Mallela, and Y. Agarwal, "MLIoT: An end-to-end machine learning system for the Internet-of-Things," in Proceedings of the International Conference on Internet-of-Things Design and Implementation, May 18, pp.169-181, 2021.
[2] A. Gurusamy and I. A. Mohamed, "The role of AI and machine learning in full stack development for healthcare applications," Journal of Knowledge Learning and Science Technology, Vol.1, No.1, pp.116-123, 2021.
[3] K. Xu, X. Wan, H. Wang, Z. Ren, X. Liao, D. Sun, C. Zeng, and K. Chen, "Tacc: A full-stack cloud computing infrastructure for machine learning tasks," arXiv preprint arXiv:2110.01556, 2021.
[4] S. Xi, Y. Yao, K. Bhardwaj, P. Whatmough, G. Y. Wei, and D. Brooks, "SMAUG: End-to-end full-stack simulation infrastructure for deep learning workloads," ACM Transactions on Architecture and Code Optimization (TACO), Vol.17, No.4, pp.1-26, 2020.
[5] S. Prakash, T. Callahan, J. Bushagour, C. Banbury, A. V. Green, P. Warden, and V. J. Reddi, "CFU Playground: Full-stack open-source framework for tiny machine learning (TinyML) acceleration on FPGAs," in 2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), Apr., pp.157-167, 2023.
[6] H. Genc, S. Kim, A. Amid, A. Haj-Ali, V. Iyer, P. Prakash, J. Zhao, et al., "Gemmini: Enabling systematic deep-learning architecture evaluation via full-stack integration," in 2021 58th ACM/IEEE Design Automation Conference (DAC), pp.769-774, 2021.
[7] A. Saiyeda and M. A. Mir, "Cloud computing for deep learning analytics: A survey of current trends and challenges," International Journal of Advanced Research in Computer Science, Vol.8, No.2, 2017.
[8] J. M. Dharmalingam and M. Vadlamaani, "A novel intelligent agent equipped with machine learning for route optimization in effective supply chain management for seasonal agricultural products," International Journal of Computer Information Systems and Industrial Management Applications, Vol.16, No.3, pp.18-18, 2024.
[9] G. Ruchitha, D. Jyoshna, G. S. K. Galla, I. S. M. Varma, C. M. Babu, and L. Pallavi, "BookGenius: Elevating reading exploration through full-stack mastery," in 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT), Aug., Vol.1, pp.443-448, 2024.
[10] C. Jansen, J. Annuscheit, B. Schilling, K. Strohmenger, M. Witt, F. Bartusch, C. Herta, P. Hufnagl, and D. Krefting, "Curious Containers: A framework for computational reproducibility in life sciences with support for deep learning applications," Future Generation Computer Systems, Vol.112, pp.209-227, 2020.

Thiyagarajan Mani Chettier ,Venkata Ashok Kumar Boyina, "Integrating Machine Learning with Fullstack Development Using ML.NET," International Journal of Computer Sciences and Engineering, Vol.13, Issue.1, pp.17-23, 2025.

An optimized Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) together (GA-PSO) method for email spam classification is presented in this paper. To improve classification accuracy and computing efficiency, the model combines The collective intelligence found in Particle Swarm Optimization (PSO).with the evolutionary powers of Genetic Algorithms (GA). The proposed GA-PSO classifier was rigorously tested over 400 cycles using datasets from Enron and Spam Assassin. Superior performance measures were attained by the model, including a 50% improvement in fitness margin, a 3% decrease in fitness error margin, and a computational efficiency that was five times faster than traditional techniques. By developing a strong, scalable algorithm with enhanced decision-making accuracy, this research advances spam detection and makes a substantial advancement in tackling email spam issues.

Email Spam, Machine Learning, Genetic Algorithm, Particle Swarm Optimization.

[1] A. Bhowick and S. M. Hazarika, “Machine Learning for E-Mail Spam Filtering: Review, Techniques and Trends,” Springer Nature, Vol.443, No.7, pp.1-8, 2017.
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[3] H. Faris, A.-Z. M. Ala, A. A. Heidari, I. Aljarah, M. Majdi, H. A. Mohammad and H. Fujita, “An Intelligent System for Spam Detection and Identification of the most Relevant Features based on Evolutionary Random Weight Networks,” Information Fusion, August, Vol.48, No.3, pp.67-83, 2019.
[4] V. S. Wakade, “Classification of Image Spam.,” OhioLINK Electronic, Vol.1, No.1, pp.1-5, 2011.
[5] O. E. Taylor and S. P. Ezekiel, “A Model to Detect Spam Email Using Support Vector Classifier and Random Forest Classifier,” International Journal of Computer Science and Mathematical Theory, Vol.6, No.1, pp.1-11, 2020.
[6] D. Melvin, T. Celik and C. Van Der Walt, “Unsupervised feature learning for spam email filtering,” Computers & Electrical Engineering, March, Vol.74, No.3, pp.89-104, 2019.
[7] G. Sanghani and K. Kotecha, “Incremental personalized E-mail spam filter using novel TFDCR feature selection with dynamic feature update,” Expert Systems with Applications, January, Vol.115, No.9, pp.287-299, 2019.
[8] O. H. Odukoya, O. B. Adedoyin, B. I. Akhigbe, T. A. Aladesanmi and G. A. Aderounmu, “An architectural-based approach to detecting spam in electronic means of communication,” Nigerian Journal of Technology, Vol.37, No.3, pp.1-5, 2018.
[9] P. Pandey, C. Agrawal and T. N. Ansari, “A Survey: Enhance Email Spam Filtering,” International Journal of Advanced Technology & Engineering Research (IJATER), Vol.8, No.1, pp.1-7, 2018.
[10] R. D. Warkar and I. R. Shaikh, “Review On: Detection of Spam Comments Using NLP Algorithm,” International Journal of Engineering And Computer Science, January, Vol.7, No.1, pp.23386-23489, 2018.
[11] F. Wentao, S. Bourouis, N. Bouguila, F. Aldosari, H. Sallay and J. K. Khayyat, “EP-Based Infinite Inverted Dirichlet Mixture Learning: Application to Image Spam Detection,” Springer Science, Vol.1, No.11, pp.342-354, 2018.
[12] I. Androutsopoulos, G. Paliouras, V. Karkaletsis, G. Sakkis, C. D. Spyropoulos and P. Stamatopoulous, “Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach,” in the proceedings from the fourth European Conference on Principles and Practice of Knowledge Discovery in Database, Lyon, France, 2000.
[13] A. B. Singh, S. B. Singh and K. M. Singh, “Spam Classification Using Deep Learning Technique,” International Journal of Computer Sciences and Engineering, 31 May, Vol.6, No.5, pp.383-386, 2018.
[14] C. Neelam and D. Nitesh, “Spam Detection Approach Using Modified Pre-processing With NLP,” International Journal of Computer Sciences and Engineering, May, Vol.7, No.10, pp.158-161, 2019.
[15] H. A. Meaad, A. A. Mohammed and A. H. Mohd, “Advancing Email Spam Classification using Machine Learning and Deep Learning Technique,” Engineering, Technology & Applied Science Research, 14 May, Vol.14, No.4, pp.14994-15001, 2024.

Domo Omatsogunwa Ereku, Vincent I.E. Anireh, Onate Egerton Taylor, "Model For Email Spam Classification Using Hybrid Machine Learning Technique," International Journal of Computer Sciences and Engineering, Vol.13, Issue.1, pp.24-32, 2025.

The Gore English School Chousala website is designed to provide a centralized platform that enhances communication, accessibility, and engagement for students, parents, and staff. This project aims to streamline essential information, including academic updates, event notifications, and resource sharing, into a user-friendly and visually appealing interface. Using modern web development techniques, the site emphasizes intuitive navigation, responsiveness across devices, and an aesthetic that reflects the school’s values. Key features include interactive admission forms, real-time announcements, and a dedicated section for academic achievements. The website ensures data security and ease of access, catering to a broad audience with varying technical expertise. This initiative highlights the significance of digital transformation in educational institutions, fostering stronger connections within the school community while promoting a seamless exchange of information.

Educational Platform, Digital Transformation, School Website Design, Interactive Communication, Student Engagement, Academic Resources, User-Friendly Interface, Responsive Web Development.

[1] SVERI College of Engineering, 1. "Official Website of SVERI College of Engineering," *Journal of Engineering Education*, Vol.1, Issue.1, pp.1-5, 2022.
[2] Lotus English School, "Lotus English School," *Journal of Education and Learning*, Vol.1, Issue.2, pp.6-10, 2022.
[3] Kawathekar Prashala, "Kawathekar Prashala, Solapur," Journal of School Management, Vol.3, Issue.1, pp.11- 15, 2022.
[4] Karmayogi Public School, “Karmayogi Public School, Maharashtra,” Journal of Education and Community Development, Vol.4, Issue.2, pp.16-20, 2022.
[5] HBS Junior College, “Official Website of HBS Junior College,” Journal of Higher Education, Vol.5, Issue.2, pp.21-25, 2022.
[6] Aarya Public School, “Aarya Public School Website,” Journal of School Administration, Vol.2, Issue.3, pp.26-30, 2022.
[7] Arihant Public School, “Arihant Public School CBSE,” Journal of Educational Standards, Vol.3, Issue.2, pp.31-35, 2022.
[8] Sinhgad Public School, “Sinhgad Public School, Pandharpur,” Journal of Community Education, Vol.4, Issue .1, pp.36-40, 2022.
[9] Apte Uplap Prashala, “Apte Uplap Prashala Pandharpur on Facebook,” Journal of Social Media in Education, Vol.1, Issue.4, pp.41-45, 2022.
[10] D.Y. Patil Vidyaniketan, “D.Y. Patil Vidyaniketan CBSE School,” Journal of Educational Institutions, Vol.2, Issue.4, pp.46-50, 2022.

Maheshwari Patil, Pallavi Mote, Samruddhi Nanaware, Vidya Todkar, Rupali Phuge, "Developing a Comprehensive Website for ‘Gore English School’: An Analytical Study," International Journal of Computer Sciences and Engineering, Vol.13, Issue.1, pp.33-40, 2025.

The Lokvikas Dairy web application project aims to serve as a prototype for an efficient, user-centric, and digitally integrated platform tailored for dairy businesses. The proposed application envisions the digital transformation of traditional dairy operations by offering tools for managing customer interactions, showcasing products, gathering feedback, and ensuring administrative efficiency. This conceptual design reflects the potential of modern web technologies to address challenges in managing customer feedback, product visibility, and real-time data synchronization. While the product is still in development, the outlined methodology and anticipated outcomes emphasize its promise to meet the needs of both end-users and administrators. This project is structured around the core objective of creating a modular, scalable, and user-friendly solution. The envisioned system is designed to bridge the gap between businesses and their customers by offering an interactive digital space for exploring products, submitting reviews, and contacting the business. Firebase Realtime Database has been proposed as the backbone for managing and storing data, ensuring security and efficiency. Through this framework, the project aligns itself with industry standards in creating a reliable digital platform.

Online Dairy Sales, E-commerce Platform, Dairy Product Management, Inventory Management, Bulk Order Management.

[1] Mother Dairy, “Enhancing Dairy Supply Chain with Technology,” Journal of Dairy Innovations, Vol.12, Issue.2, pp.45-50, 2020.
[2] Karnataka Milk Federation (KMF), “Cooperative Models in Dairy Supply Chains,” International Journal of Dairy Farming, Vol.8, Issue.3, pp.123-130, 2019.
[3] Parag Milk Foods Ltd., “Innovations in Dairy Product Development and Supply Chain,” Dairy Industry Journal, Vol.5, Issue.1, pp.34-40, 2021.
[4] Hatsun Agro Products Ltd., “Modern Dairy Practices and Cold Chain Logistics in India,” Journal of Food Distribution, Vol.6, Issue.2, pp.71-78, 2018.
[5] Aavin, “Cooperative Dairy Supply Chains in South India: A Case Study of Aavin,” South Indian Dairy Review, Vol.7, Issue.2, pp.55-62, 2020.
[6] Dynamix Dairy Industries Ltd., “AI in Dairy Inventory Management and Forecasting,” International Journal of Dairy Technology, Vol.9, Issue.4, pp.12-17, 2021.
[7] Heritage Foods Ltd., “Supply Chain and Social Responsibility in Dairy Operations,” Indian Dairy Research Journal, Vol.3, Issue.3, pp.88-92, 2019.
[8] Milma (Kerala Co-operative Milk Marketing Federation), “Community-Based Supply Chain in Dairy,” Cooperative Dairy Management Journal, Vol.4, Issue.1, pp.43-49, 2020.

Prashant Bhandare, Omkar Jadhav, Indrajit Raut, Vaibhav Godase, Vishwanath Kulkarni, "Lokvikas Milk and Dairy Products: An Online Dairy Product Sales and Management System," International Journal of Computer Sciences and Engineering, Vol.13, Issue.1, pp.41-47, 2025.

Community Wireless Mesh Networks (WMNs) have emerged as a cost-effective ‘last-mile’ solution to network services in rural low-income regions primarily located in Sub-Saharan Africa and Developing Asia. However, researchers have often criticized the WMNs for experiencing degraded Quality of Service (QoS), particularly in terms of latency, jitter, and packet loss, as network size and number of users increase. This study investigates the performance of latency, jitter, and packet loss in a multi-hop WMN environment using QoS sensitive voice traffic and two widely used routing protocols - Optimized Link State Routing (OLSR) and Ad hoc On-Demand Distance Vector (AODV) – to determine the most suitable mesh routing protocol for future scalability experiments. Given the strict QoS requirements of voice traffic, the research assumes that a WMN that scales well under voice traffic has the potential to scale well for a broader range of network applications. Using Network Simulator-3 (NS-3), one-way latency, jitter, and packet loss percentage (PL%) were evaluated for up to five simultaneous VoIP calls over a 9-hop WMN topology. The results indicate that while both OLSR and AODV maintained packet loss below 1%, OLSR consistently outperformed AODV in terms of lower latency and jitter. The findings suggest that OLSR is better suited for supporting real-time voice applications in WMNs. Future work will extend this research to analyse WMN scalability under video traffic, explore alternative network topologies (e.g., grid-based WMNs), integrate mobile phones, and evaluating the impact of MIMO routers and varying signal strengths. This study contributes to the ongoing efforts to quantify WMN scalability so that they can be optimally deployed as a community-driven Internet infrastructure.

Wireless Mesh Network (WMN), VoIP, OLSR, AODV, Latency, Jitter, Packet Loss, Network Scalability

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[3] S. Om, “Voice and rural wireless mesh community networks: a framework to quantify scalability and manage end-user smartphone battery consumption,” Ph.D. Thesis, Dept. Computer Science, Univ. of the Western Cape, Cape Town, 2021.
[4] C. Rey-Moreno, Z. Roro, W. D. Tucker, M. J. Siya, N. J. Bidwell, and J. Simo-Reigadas, “Experiences, Challenges and Lessons from Rolling Out a Rural WiFi Mesh Network,” In the roceedings of the 3rd ACM Symposium on Computing for Development, ACM, USA, Article 11, 2013. doi: 10.1145/2442882.2442897.
[5] P. Zikomangane, “A Free Wireless Network in the DRC: An Answer to Internet Shutdowns and Exorbitant Access Costs,” in Global Information Society Watch 2018 Community Networks, A. Finlay, Ed., Association for Progressive Communications (APC), ISBN: 9789295113060, 2018, pp.111–114, 2018.
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[8] D. Johnson, “Re-architecting Internet Access and Wireless Networks for Rural Developing Regions,” Ph.D. Thesis, Dept. Computer Science, Univ. of California at Santa Barbara, 2013.
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Shree Om, "Scalability of Voice Traffic in OLSR and AODV Mesh: Preliminary Experiments," International Journal of Computer Sciences and Engineering, Vol.13, Issue.1, pp.48-55, 2025.

The FarmBuddy App helps people easily find and buy fresh fruits and vegetables. It helps to customer discover nearby farmers markets, see what suppliers have available, and learn about where the products come from and their prices. For farmers, the app acts as a marketplace to features their products, keep track of what they have for sale, and connect directly with customers. The Farmers Market App is a dynamic mobile platform designed to connect local farmers directly with consumers, fostering the farm-to-table experience while promoting sustainable agriculture. As demand for fresh, organic produce rises, this app addresses the growing consumer interest in supporting local economies and accessing quality food. By offering real- time inventory updates, location-based services, and online ordering capabilities, the app enhances convenience and efficiency for both farmers and consumers. Additionally, it cultivates community engagement through user reviews and educational resources, empowering users with knowledge about sustainable practices and seasonal eating. Ultimately, the Farmers Market App aims to revolutionize the local food system, creating a vibrant marketplace that supports farmers and nurtures healthier communities. The Farmers Market App is an innovative mobile platform designed to connect local farmers with consumers, enhancing the farm-to- table experience while promoting sustainable agriculture. This app addresses the increasing demand for fresh, organic produce and the desire for consumers to support local economies.

Marketplace, Fresh Product, Organic Products, Real-Time Tracking, Customer Support, Empowering, Agriculture

[1] Otipy: A Health & Wellness App," Google Play Store, Vol.1, Issue.1, pp.11, 2025
[2] EgreensPacker: A Comprehensive Review - Google Play Store, Vol.1, Issue.1, pp.1-5, 2025.
[3] iFresh, “Customer App Overview,” In the Google Play Store, pp.1-4, 2025.
[4] AmazonFresh: Exploring Products and Offers - Amazon India, Vol.1, Issue.1, pp.1-5, 2025.
[5] BigBasketMandi: Revolutionizing Online Grocery Shopping - Google Play Store, Vol.1, Issue.1, pp.1-6, 2025.
[6] Instacart: Innovative Approaches to Online Grocery Shopping – Journal of E-Commerce and Technology, Vol.7, Issue.1, pp.10-15, 2025.
[7] Classibiz App: Innovative Solutions for Business Listings – Journal of Mobile Applications and Technology, Vol.3, Issue.5, pp.45-50, 2025.
[8] Farmers Stop. Farmers Stop: Your Online Grocery Shopping Destination. Vol.5, Issue.2, pp.15-20, 2025.
[9] JioMart: Fresh Fruits Online Shopping – Journal of E-Commerce and Retailing, Vol.6, Issue.3, pp.22-27, 2025.
[10] KisanKonnect: Revolutionizing Agricultural Supply Chains – Journal of Agricultural Technology and E-Commerce, Vol.5, Issue.2, pp.30-35, 2025.

Shital Karande, Shruti Menkudale, Ashwini Chopade, Maya Darane, Gayatri Jadhav, "FarmBuddy: Connecting Farmers and Customers for Local, Quality Produce," International Journal of Computer Sciences and Engineering, Vol.13, Issue.1, pp.56-63, 2025.

The application of text mining in natural language processing (NLP) has gained significant attention in recent years, particularly for tasks such as grammar correction, syntactic parsing, and error detection. One of the promising approaches for addressing these tasks is the use of Convolutional Neural Networks (CNNs), which, although originally designed for image recognition, have proven highly effective in extracting hierarchical patterns from sequential data, including text. This paper explores the application of CNNs for English grammar correction, leveraging their ability to identify local dependencies and complex grammatical structures within sentences. The approach involves training CNN models on large corpora of annotated text to automatically detect and correct grammatical errors, such as subject-verb agreement issues, tense inconsistencies, and word order mistakes. By convolving over word sequences, CNNs are capable of recognizing syntactic relationships and learning contextual cues that help in distinguishing grammatically correct forms from errors. The paper also discusses the benefits of CNN-based grammar correction, including improved accuracy, scalability, and the ability to adapt to diverse linguistic contexts. Experimental results demonstrate the effectiveness of this method compared to traditional grammar correction techniques, highlighting its potential for enhancing automated writing assistance tools, language learning applications, and real-time text editing systems. Ultimately, the integration of CNNs in text mining for grammar correction represents a promising avenue for advancing automated language processing systems and improving the efficiency of text-based communication.

Natural Language Processing (NLP), Text mining (TM), Convolutional Neural Networks (CNNs),English Grammar.

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Shankarayya Shastri, Anusha, Nisha K., Shilpa R.N., "Application of Text Mining using Convolutional Neural Network for English Grammar Correction," International Journal of Computer Sciences and Engineering, Vol.13, Issue.1, pp.64-70, 2025.