International Journal of Innovations in Science & Technology
Not a member yet
813 research outputs found
Sort by
A Comparative Evaluating Auditing Tools for Unverified Smart Contracts on Ethereum Blockchain
Full text linkThe Ethereum blockchain has transformed decentralized finance (DeFi) and is widely used to issue ERC20 tokens. However, many of these tokens rely on unverified smart contracts, which pose serious security risks. Hackers can take advantage of vulnerabilities in these unverified ERC20 tokens, leading to scams, financial losses, and a decline in user trust. Although several tools are available to audit smart contracts, their effectiveness in analyzing unverified ERC20 tokens remains uncertain. This study examines three auditing tools HoneyBadger, Maian, and Mythril by testing how well they detect security issues in unverified ERC20 tokens. The SmartBugs framework was used to support the auditing process, enabling parallel execution, standardized reports, and bulk auditing of contracts. For a thorough evaluation, two datasets were used: one from 50,581 Ethereum blockchain blocks and another from the DappRadar list of blacklisted ERC20 tokens. These datasets were chosen to provide a broad and realistic view of how the tools perform on both typical and high-risk contracts. The tools were compared based on their ability to detect issues, their execution speed, and their overall effectiveness. The results revealed clear differences in performance: some tools were better at finding vulnerabilities accurately, while others focused more on speed than depth. This study emphasizes the need to improve smart contract auditing methods and highlights the importance of developing more effective security tools to strengthen the Ethereum blockchain
Research Article Hydrothermal Synthesis and Characterization of Zinc Oxide (ZnO) Nanoparticles for Glucose Sensor
No full textZinc oxide (ZnO) nanoparticles have gained notable attention for their multifunctional role in biomedical applications, particularly in non-enzymatic glucose sensing. In this work, the hydrothermal synthesis of highly crystalline ZnO nanoparticles with controlled morphology and size is achieved under optimized reaction parameters. Comprehensive physicochemical characterizations were performed using X-ray diffraction (XRD), and UV-Vis spectroscopy, confirming the formation of phase-pure hexagonal wurtzite ZnO with nanoscale dimensions and high surface purity. The optical analysis revealed a direct bandgap energy of ~3.3 eV, supporting efficient electron transfer kinetics. Electrochemical investigations demonstrated excellent glucose sensing performance, including long-term stability, high sensitivity, rapid response, high sensitivity, low detection limit, rapid response time, and long-term stability, attributed to the enhanced surface reactivity and electron transport of the nanostructures. These findings not only advance the understanding of ZnO nanostructures in glucose biosensing but also position hydrothermally synthesized ZnO nanoparticles as a cost-effective and scalable candidate for integration into next-generation biomedical diagnostic devices
Fingerprint Based Smart Digital Life Certificate Using Mobile Technology
Full text linkA pension plan is a savings solution for pensioners that plays a vital role in pensioner’s life after retirement. Different pension disbursing systems have been implemented which aim to support individual pensioners after retirement. This study highlights several critical issues of pensioners. Most of the pensioners are of old age, and it is difficult for them to move physically towards the concerned authority for life authentication in a periodic manner. This study proposes a model for pension disbursing based on the fingerprint scanner enabled smartphone. The proposed model is designed for pensioner’s bi-annual authentication and issuance of Digital Life Certificate (DLC) ubiquitously. The proposed model eliminates the physical presence and travelling expenses. The Hammer and Champy methodology are utilized to construct the model while the Delphi method is used for evaluating the proposed model. This study involves quantitative research to investigate the behavioural intention to accept fingerprint scanner enabled smartphone for the pension receiving process. The data was analysed by applying the goodness-of-fit Chi-square test to inspect the efficiency and impact of the adoption of mobile-based biometric fingerprint system (MBFPS) for the pension disbursing system
Analysis of Social Media Imagery for Crisis Management Applications
Full text linkSocial media data holds immense potential for real-time disaster response. This study explores leveraging deep learning to automatically detect disaster-related information across various social media platforms. By analyzing the performance of different models in identifying relevant content, we aim to reduce information gathering delays and support timely rescue efforts. Faster information gathering translates to quick deployment of rescue teams, potentially saving lives and minimizing property damage. We evaluate these models on a benchmark dataset and explore the potential of combining them for even greater accuracy. Among the models, VGG16 achieved an accuracy of 81% in identifying disaster-related content. Additionally, exploring different fusion techniques for combining these models further improved accuracy to 83% with Hybrid Fusion. This research offers valuable insights for future exploration of deep learning techniques in disaster management
AI-Sentinel: A Novel AI-Powered Intrusion Detection Approach Against Cyber Threats for In-Vehicular Communication Systems
Full text linkThe emergence of revolutionizing technologies such as Artificial Intelligence and the Internet of Things, and their integration into the automotive industry has brought innovations and made the lives of common people easier and complacent. Leveraging the advanced intelligent services provided by the connected and autonomous vehicles the driving experience is much more convenient and effortless. The CAN (Controller Area Network) protocol is the most commonly deployed protocol in in-vehicular communications in the ICVs (intelligent connected vehicles) environment due to its efficiency and speed. However, it lacks basic security mechanisms like encryption and authentication making it vulnerable to various cyber threats. In this article, we have presented a novel, robust, cutting-edge AI-based Intrusion detection system for detecting various seen and unseen cyber-attacks in in-vehicular networks to ensure security. Two main models deployed in the proposed framework are RNN for dealing with temporal dependencies in the CAN traffic and LightGBM for efficient feature extraction. The experimental results show that the hybrid of these two models performs better in terms of various evaluation metrics, with its accuracy being 94% in classifying the CAN traffic into normal and different attack classes. A comparison with the existing state-of-the-art approaches shows that our proposed approach is more robust and secure, with it being deployed in a Federated Learning FL environment
Comparative Analysis of Different Feeding Techniques and Different Substrates on the Performance of 5G Micro-strip Patch Antenna
Full text linkWireless communication is evolving rapidly to meet growing demands for higher data rates and seamless connectivity, especially with the rise of the Internet of Things (IoT). Among the latest advancements, 5G technology stands out by enabling ultra-fast data transmission, high capacity, and efficient spectrum utilization through millimeter-wave frequencies. This study presents a comparative analysis of six Microstrip Patch Antennas (MPAs) designed for 5G applications, addressing the challenge of limited space and increasing performance demands. The novelty of this work lies in the evaluation of how substrate materials and feeding techniques influence MPA performance, providing insights not thoroughly addressed in prior research. The antennas were designed using three substrates1: FR-4 (εr = 4.4), 2-Rogers RT5880 (εr = 2.2), and 3- Taconic RF-35TC (εr = 3.5)—and two feeding techniques: Microstrip line feed and coaxial probe feed. All antennas were tuned to resonate at 38 GHz, suitable for 5G millimeter-wave applications. Feeding technique also significantly affects impedance matching and Gain. It is found out that using Roggers RT Duroid 5880 substrate with Microstrip feedline technique provides the highest gain whereas the largest bandwidth is achieved using coaxial feed with FR4 substrate. A quarter-wave transformer was additionally implemented for optimal impedance matching between the source and antenna. The findings guide substrate and feed selection in compact 5G antenna designs
Extreme Flooding in Pakistan: An AI-Powered Framework for Enhanced Urban Flood Management System
Full text linkUrban flooding poses considerable challenges for metropolitan areas, contributing to rapid urbanization and significant climatic change. This research develops a machine learning-based Urban Flood Management System (UFMS) to predict and manage flood risks, incorporating an enhanced risk warning system for rapidly urbanizing areas. The mitigation of urban flooding parameters, such as rainfall intensity, humidity, temperature, soil moisture, land use, and drainage network capacity, is analyzed in the UFMS. The system employs the artificial intelligence model Support Vector Machine (SVM), in aggregation with ARIMA modeling, to attain a remarkable accuracy rate of 99.99% to forecast flood events. The model undergoes training with two decades of historical meteorological data to augment its predictive prowess and guarantee robust performance. The result shows that SVM performs with superior accuracy in comparison to other machine learning algorithms (MLAs) by effectively handling complex, multidimensional and multimodal data. This hybrid methodology provides real-time and highly accurate prediction of upcoming floods that leads to actionable insights for urban planners and emergency response teams. Future improvements may involve the utilization of real-time data obtained from Internet of Things (IoT) nodes combined with an advanced deep learning model to improve forecast accuracy, scalability and reduce response time, which will lead to minimizing damages
The Role of Industries in Accelerating Climate Change: A Case Study of Karachi (SITE Industrial Area)
Full text linkKarachi, Pakistan, is a densely populated city with a strong industrial presence, and it is increasingly threatened by climate change. This includes rising temperatures and changes in rainfall patterns. This paper examines how these climatic changes affect key industries in Karachi. It looks at how higher temperatures and limited water resources, intensified by the city\u27s extensive concrete and industrial development, create operational and economic challenges for various sectors. This study utilized a combination of satellite datasets (Landsat 8 and 9), climatic data (CHIRPS), and ancillary data (KDA maps) to analyze environmental changes in Karachi\u27s SITE area from 2015 to 2025. The analysis included rainfall analysis, LULC change detection, NDVI and LST trend analysis, and random point sampling for site-specific correlation. The presented results are accompanied by a narrative interpretation of environmental changes and their implications on the SITE industrial zone. This study examines the impact of climate change and urban-industrial growth on Karachi\u27s SITE area from 2015 to 2025. The findings reveal environmental stress due to declining vegetation cover and rising land surface temperatures, likely driven by unregulated industrial expansion and rainfall variability. However, signs of ecological recovery in 2025 suggest potential benefits from natural regeneration or better land management. To enhance climate resilience, the study recommends promoting urban greening, controlling industrial sprawl, improving water management, adopting climate-friendly practices, and regular monitoring using satellite data and GIS tools. By adopting climate-resilient practices and transitioning to low-carbon technologies, Karachi\u27s industries can reduce their environmental footprint and contribute to a more sustainable future
Climate Change and the Changing Rainfall Patterns in Karachi
Full text linkAbrupt weather phenomena, including heat waves, frequent intense storms, outbreaks of forest fires, glacier melting, and flash floods are experienced throughout the world. Pakistan lies in the South Asian region that falls in the monsoon climatic regime, experiencing summer rainfall. The city of Karachi, which is also highly urbanized situated in southern Sindh and receives secondary monsoon rainfall from the month of July to September. During the 1960s, the city received appreciable rainfall during the monsoon, but the amount of rainfall started declining during the 1980s. At the end of the 20th century, the rainfall pattern was quite abrupt, associated especially with the passage of cyclones, which developed in the Arabian Sea and, after touching Oman, reached Karachi, or, moving from Gujrat in India, reached Karachi. So, the rainfall which received annually is now received within a day. The study represents the statistical analysis as well as the GIS and remote sensing perspective of the changing patterns over the last fifty years
Self-Powered Robots – A Survey
Full text linkSelf-powered robots represent a significant advancement in autonomous robotics, leveraging renewable energy sources such as solar panels, thermoelectric generators, piezoelectric actuators, microbial fuel cells, and RF energy harvesting to operate independently of traditional power supplies. This study presents a comparative analysis of seven self-powered robotic systems, including the Crabbot, Thermoelectric Quadruped, MilliMobile, and Row-bot, evaluating their energy mechanisms, power consumption, control systems, and application domains. Notable findings include the Crabbot\u27s 85 nm resolution and 150 V piezoelectric actuation for precision tasks, the Thermoelectric Quadruped\u27s 703 J/m gait energy cost for geothermal monitoring, and MilliMobile\u27s submillimeter-scale battery-free operation via RF harvesting. These robots are assessed based on critical parameters such as load-to-weight ratio, energy autonomy, and control architecture. The study highlights the growing role of miniaturized, energy-efficient designs in enabling real-world deployment across sectors like pipeline inspection, remote environmental sensing, and disaster response. By identifying performance benchmarks and gaps, this paper offers insight into next-generation, self-sufficient robotics aimed at sustainability, reliability, and broader societal impact