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,

Lightweight Smart Contract-Based Secure Healthcare Data Management over Edge-IoMT Networks

Govind Kumar Mishra — 2026-05-06

The rapid growth of the Internet of Medical Things (IoMT) has transformed smart healthcare
by enabling real-time monitoring through wearable sensors and connected medical devices.
However, secure management of sensitive healthcare data remains a major challenge due
to privacy risks, unauthorized access, and centralized vulnerabilities. This paper proposes
a lightweight smart contract-based secure healthcare data management framework over
edge-enabled IoMT networks. The proposed architecture integrates edge computing and
blockchain technology to provide secure, decentralized, and low-latency healthcare services.
Edge gateways perform data aggregation and encryption, while lightweight smart contracts
enable secure authentication, access control, and immutable medical record management.
A lightweight consensus mechanism is incorporated to reduce computational overhead and
transaction delay in resource-constrained healthcare environments. Experimental analysis
demonstrates improved security, reduced latency, enhanced scalability, and efficient data
management compared with conventional blockchain-based healthcare systems.

A Novel Harmony Search Framework for Sinkhole Attack Recognition in Wireless Sensor Networks

Avinash chaubey — 2026-05-06

A Wireless Sensor Network (WSN) is a system of interconnected sensor nodes that can detect and exchange data wirelessly over very small distances. The communication aspect of WSN makes security a major concern. There have been a number of attacks against the sensor nodes. In a sinkhole attack, a node in the WSN falsely claims to be the one with the shortest route to another node, either the sink or the destination. In order to identify the sinkhole attack, researchers put forth several methods. In this research work, Revamped Harmony Search is employed to detect sinkhole nodes in WSN. The proposed model is compared with 4 other existing techniques to prove its significance. The results are interpreted with different level of imposing sinkhole nodes in the network and on different patterns. On an average, the proposed HRS method significantly outperforms the existing methods.

Bicycle Level of Service Evaluation and Accident Analysis of Urban Road Networks in Jaipur City

Vikash Kumar — 2026-06-28

The roads of Jaipur city are tight and intruded by further activities. Bus amenities in precise have worsened, and their competence and service quality have deteriorated, prompting commuters to personalize mode and IPL. This not only restricts the traffic flow, but also endangers the lives of road users. The total number of fatal accidents as well as related accidents in the city has been increasing over the years. People killed per 100 accidents during the year 2018 are alarmingly high, as many as 28. Pedestrian deaths are also very high as a percentage of road accidents. During current years, they found more than 85% of all road deaths. BLOS is essentially an ordering problematic and cluster analysis is considered the utmost appropriate technique to solve this classification problem. The ASW is calculated for each clustering technique to associate between clustering techniques and find the best clustering method. K-Mean cluster analysis is establish to be the utmost appropriate technique in significant BLOS limits for the level of service kinds of urban roads in the Indian context.

Keywords: Road safety, fatal accidents, Pedestrian deaths, NH-11, NH-8

Enhancing Mechanical Properties and Durability of Plain Cement Concrete with Basalt

Vikash Danwar — 2026-06-29

The present study investigates the combined effect of basalt fibers (0–8% by weight of cement) and silica fume (10% cement replacement) as sustainable additives to enhance the mechanical and durability properties of plain cement concrete (PCC) for road construction. Concrete mixes were prepared as C0–C4 corresponding to 0%, 2%, 4%, 6%, and 8% basalt fiber content. A suite of tests including compressive, split tensile, and flexural strength, water absorption, chloride penetration, freeze–thaw resistance, rebound hammer, and ultrasonic pulse velocity were conducted on laboratory specimens. Results indicate that strength generally improved with fiber addition, with the best overall performance around 6% fiber (C3). At later ages, compressive strength reached ~40 MPa (C3), split tensile strength reached ~6.1 N/mm² (C4), and flexural strength peaked at ~6.4 MPa (C3). Durability improved substantially: water absorption reduced from ~6.0% (C0) to ~3.0% (C4), and freeze–thaw performance improved with reduced mass loss and higher strength retention. Although workability decreased with higher fiber content, it remained manageable using superplasticizer and the densifying effect of silica fume. Overall, the combined use of basalt fibers and silica fume offers a promising approach for producing durable and sustainable PCC for road infrastructure.

Keywords: Basalt Fiber, Silica Fume, Concrete Durability, Mechanical Strength, Water Absorption, Chloride Penetration, Freeze-Thaw Resistance, Sustainable Roads, High-Performance Concrete

Treatment and Recycling Strategies for Greywater Originated from Bathrooms

Sumit Bansal — 2026-06-29

Greywater treatment and reuse have emerged as sustainable and cost-effective alternatives to address water scarcity. Conventional treatment methods, such as membrane filtration and biological systems, are often limited by high cost and sensitivity to operational variations. This study investigates an integrated treatment approach combining coagulation–flocculation, filtration, and disinfection for greywater generated from laundry and bathing sources.

Alum and a high molecular weight polyelectrolyte were used as coagulant and flocculants, respectively. Process parameters were optimized using Response Surface Methodology (RSM). The optimum dosages were found to be 9 mg/L alum and 0.8 mg/L polyelectrolyte, with mixing speeds of 140 rpm (rapid) and 18 rpm (slow), and an optimal sedimentation time of 70 minutes. A dual media filtration system comprising sand and granular activated carbon (GAC) was optimized with a GAC-to-sand ratio of 0.6.

Column studies revealed that COD removal efficiencies ranged from 62.14% to 75.73% after 1 hour, while MBAS removal reached up to 93.81% under optimal conditions. The study demonstrates that the proposed hybrid system is efficient, economical, and suitable for decentralized greywater reuse applications.

Keywords:

Greywater treatment, Coagulation–flocculation, GAC, RSM