iosr-JCE  Volume-28 ~ Issue-2 ~ Mar. - Apr. 2026

Abstract: Artificial intelligence-based advisory systems are increasingly being explored as tools to improve agricultural decision-making; however, many existing solutions assume sta- ble connectivity, uniform user literacy, and low-risk operating conditions. Such assumptions limit their applicability in rural farming environments, where language diversity, infrastruc- ture constraints, and advisory errors can significantly impact livelihoods and ecological sustainability. This paper proposes a context-adaptive agricultural advisory framework implemented through a conversational interface, designed to support farmers operating under low-connectivity and low-literacy conditions.....

Key Word: Agricultural advisory systems, context-adaptive AI, conversational interfaces, rural agriculture, decision support systems

[1] S. Wolfert, L. Ge, C. Verdouw, And M.-J. Bogaardt, “Big Data In Smart Farming—A Review,” Agricultural Systems, Vol. 153, Pp. 69–80, 2017.
[2] R. K. Ravisankar And P. S. Reddy, “Information And Communication Technologies For Agricultural Extension In India,” Indian Journal Of Agricultural Sciences, Vol. 89, No. 3, Pp. 421–427, 2019.
[3] A. Mittal, S. S. Tripathi, And R. Shukla, “Sms-Based Agricultural Advisory Services: An Evaluation Of Farmer Adoption,” Information De- Velopment, Vol. 34, No. 2, Pp. 152–164, 2018.
[4] M. Aker, C. Ksoll, And T. Lybbert, “Can Mobile Phones Improve Agricultural Outcomes? Evidence From A Randomized Experiment,” World Development, Vol. 99, Pp. 34–48, 2017.
[5] P. V. Reddy And A. S. Reddy, “Digital Platforms For Agricultural Extension Services: Opportunities And Challenges,” Journal Of Rural Development, Vol. 38, No. 4, Pp. 567–584, 2019.

Abstract: Digital transaction systems, such as virtual top-up platforms, are widely used for mobile airtime and data purchases. While these systems provide convenience, they are vulnerable to security threats, including fraud, unauthorized access, and identity theft. Traditional authentication methods, such as passwords or simple OTPs, are no longer sufficient to protect sensitive user information and financial transactions. This paper presents a three-factor authentication system designed to enhance security in virtual top-up platforms. The proposed system combines....

Key Word: Three-Factor Authentication, OTP Encryption, Digital Transaction Security, Virtual Top-Up System, Mobile Payment Security

[1]. Adewusi, A. O., Msagusa, L. A., Imanirumva, E., Obadofin, A. O., & Ndibwile, A. F. (2025). A Hybrid Three-Factor Authentication Framework For Secure Mobile Money Services In Resource-Constrained Environments. Journal Of Financial Technology And Security Studies, 12(2), 45–63.
[2]. Ali, H. N., & Al-Dabbagh, S. S. M. (2026). A Systematic Literature Review On Biometric Authentication In Mobile Banking. F1000Research, 15, 5.
[3]. Aparicio, A., Martínez-González, M. M., & Cardeñoso-Payo, V. (2024). App-Based Detection Of Vulnerable Implementations Of OTP SMS Apis In The Banking Sector. Wireless Networks, 30, 6451–6464.
[4]. Bartłomiejczyk, P., Mondal, P. C., & Sharma, R. (2025). Risk-Based Multi-Factor Authentication Approaches For Online Financial Transactions. International Journal Of Research And Innovation In Social Science, 9(3), 3062–3076.
[5]. Hasan, S. S. U., Ghani, A., Daud, A., Akbar, H., & Khan, M. F. (2025). A Review On Secure Authentication Mechanisms For Mobile Security. Sensors, 25(3), 700.

Abstract: The rapid growth of digital payments, credit card fraud is now a big worry for banks and other financial institutions and users. Traditional fraud detection systems mainly focus on identifying whether a transaction is fraudulent or not, but they do not provide information about the type of fraud. This project proposes A method for finding....

Key Word: Credit Card Fraud Detection, Multi-Class Clas-sification, Machine Learning, Random Forest, PaySim Dataset, Financial Transactions, Data Imbalance, Fraud Classification, Predictive Modeling, Confusion Matrix

[1] R. Sailusha, V. Gnaneswar, R. Ramesh, And G. R. Rao, “Detection Of Credit Card Fraud Through Machine Learning Methods,” In Proc. 4th Int. Conf. Intelligent Computing And Control Systems (Iciccs), Ieee, Pp. 1264–1270, May 2020.
[2] V. N. Dornadula And S. Geetha, “Machine Learning-Based Approach For Identifying Credit Card Fraud,” Procedia Computer Science, Vol. 165, Pp. 631–641, 2019[3] J. O. Awoyemi, A. O. Adetunmbi, And S. A. Oluwadare, “Comparative Study Of Machine Learning Techniques For Credit Card Fraud Detection,” In Proc. Int. Conf. Computing Networking And Informatics (Iccni), Ieee, Pp. 1–9, Oct. 2017.
[4] A. Thennakoon Et Al., “Real-Time Fraud Detection In Credit Card Transac-Tions Using Ml Techniques,” In Proc. Int. Conf. Cloud Computing, Data Science & Engineering, Ieee, Pp. 488–493, Jan. 2019.
[5] F. K. Alarfaj Et Al., “Advanced Machine Learning And Deep Learning Methods For Credit Card Fraud Detection,” Ieee Access, Vol. 10, Pp. 39700–39715, 2022

Abstract: The emergence of agentic artificial intelligence is reshaping organizations not simply by adding new tools, but by transforming how work, decision-making, accountability and value creation are organized. Existing maturity models still describe organizational evolution mainly in terms of digitalization, AI adoption or readiness, and therefore....

Key Word: agentic AI; agentic organization; maturity model; ASM; co-agency; governance; AI-first workflows

[1]. Accenture. (2026a). The Dawn Of The Agentic Deal: How Leading Companies Unlock Value Pools Traditional M&A Can’t Reach.
[2]. Accenture. (2026b). The New Rules Of Platform Strategy In The Age Of Agentic AI: Five Priorities To Help Companies Align People, Platforms And Intelligence.
[3]. Acharya, V. (2026). Governing The Agentic Enterprise: A Governance Maturity Model For Managing AI Agent Sprawl In Business Operations. Arxiv Preprint.
[4]. AIGN – Artificial Intelligence Governance Network. (2025). AIGN Agentic AI Governance Framework 1.0.
[5]. Casale, O., Rinaldi, P., & Corradetti, I. (2026). ASM Agentic System Management. Milano: Franco Angeli.
[6]. Casale, O., Fernandez, R., Reynoso, A., Cassone, P., Rinaldi, P., & Cibien, M. (2024). Antifragility And Innovation Management Systems