International Journal of Engineering Technology and Computer Research

AI-Driven Sensor Networks for Early Flood Detection and Risk Mitigation

Vikash Kumar — 2026-05-05

The increasing frequency and severity of both extreme weather conditions and in the first-place floods in recent years developed into the long-awaited realization that there is a need to have effective monitoring and notification systems. The importance of preparatory measures in improving preparedness and response can be explained by the floods that are described as having severe implications on human beings, infrastructures as well as the natural environment. One of the significant dangers to the world population is floods; therefore, there is the necessity to have effective early warning systems that would guarantee people are evacuated and mitigated in good time. The research proposal will analyse the uses of machine learning algorithms and sensor technology in enhancing flood prediction systems and flood warning systems. The proposed system uses machine learning models as predictive analytics based on real-time data of different sensors (rainfall gauges, river level, weather stations, etc.). Regression, classification, and ensemble models are some of the models used in this study and are trained on Historical data to predict flood occurrences with better precision and lead time. The architecture of the system makes it easy to constantly acquire data, preprocess it, train the model and deploy alerts in the form of mobile applications and emergency communications channels. Precision, recall, and F1-score are evaluation metrics that show the effectiveness of the approach compared to traditional ones. The results help to highlight the prospects of incorporating superior technologies to develop the preparedness and response to floods, which helps to reduce the risks and helps to reduce the number of damage-ages and victims of floods. Future trends will involve scalability, resistance to various environmental factors, and the incorporation of new IoT systems to provide holistic disaster management systems.

Keywords: Flood Prediction, Machine Learning Algorithms, Sensor-Based Monitoring, Early Warning Systems, Real-Time Data Analytics, Internet of Things (IoT), Disaster Management and Mitigation

Recent Advances in Cybersecurity: Technologies, Threats, and Countermeasures

Krishna K. Sharma — 2026-05-05

Cybersecurity has emerged as a critical concern in the digital age, where the rapid advancement of technology, coupled with the increasing sophistication of cyber threats, poses a significant risk to individuals, organizations, and nations alike. This paper reviews recent advancements in cybersecurity technologies, examines evolving cyber threats, and discusses the latest countermeasures implemented to combat these threats. We explore various domains of cybersecurity, including network security, endpoint protection, artificial intelligence (AI)-driven defenses, blockchain, and privacy-enhancing technologies. The paper also highlights current challenges, the role of regulatory frameworks, and potential future trends that will shape the cybersecurity landscape.

A Comprehensive Survey on the Integration of Machine Learning with Secure Blockchain-Based Applications

Sejal Kumari — 2026-05-05

The rapid evolution of digital technologies has led to the convergence of Machine Learning (ML) and Blockchain, two powerful paradigms with complementary strengths. ML enables intelligent data analysis, prediction, and automation, while Blockchain ensures secure, decentralized, and transparent data management. However, when used independently, ML faces challenges related to data privacy, trust, and integrity, whereas Blockchain suffers from scalability limitations and restricted data processing capabilities. This survey explores the integration of ML with secure blockchain-based systems to overcome these challenges. It examines various architectural approaches, including onchain and off-chain ML models, federated learning integrated with blockchain, and smart contract-based automation. The study also highlights key application domains such as healthcare, finance, supply chain management, and IoT systems. Furthermore, the paper analyzes critical technical aspects like data security, consensus mechanisms, model training efficiency, and computational overhead. It identifies major challenges, including scalability constraints, high energy consumption, latency, and privacy concerns in decentralized environments. By reviewing existing research and case studies, this work provides insights into emerging trends and future directions. The findings demonstrate that integrating ML with Blockchain enhances security, transparency, and trust while enabling intelligent decision-making in distributed systems.

Keywords: Machine Learning, Blockchain, Decentralization, Smart Contracts, Data Privacy, Consensus Mechanism, Distributed Systems, Artificial Intelligence, Cybersecurity.

Artificial Intelligence: Recent Advances, Challenges, and Future Directions

Krishna Kumar Sharma — 2026-05-05

Artificial Intelligence (AI) has transformed industries, from healthcare to transportation, by enabling systems to learn, reason, and perform complex tasks with remarkable efficiency. This review examines the recent advancements in AI, focusing on novel techniques and applications, and explores the challenges hindering its broader adoption. Finally, it discusses potential future directions for AI research, emphasizing the need for ethical frameworks, robust algorithms, and interdisciplinary collaboration to maximize AI’s societal impact.

Index Terms—Artificial Intelligence (AI), Deep Learning (DL), Transfer Learning,