UCP Journal of Engineering & Information Technology (HEC Recognized-Y Category)

Floating Satellite with Ultrasonic Radar Payload for Debris Detection

2024-01-05T08:31:51+00:00

An educational platform designed for assessing and testing control algorithms for pico and nanosatellites in a near-frictionless environment is presented. The system provides access to test different space application algorithms. The application provides here is the debris detection mission for space surveillance. Different payloads are added on FloatSat, integrated with the Real-Time operating system (RODOS) and STM32F407G as the main controller to detect the debris around the satellite in a specific range.

Harmony Hub: Bridging Communities Through Digital Collaboration and Authentic Service Recognition

2024-07-19T06:44:44+00:00

"Harmony Hub" is a groundbreaking digital platform that has the ability to transform the volunteering sector. This platform brings into play transparency, trust and partnership so as to meet the complex requirements of universities, NGOs, and colleges. It guarantees that every user has a unique profile thereby able to keep comprehensive records and verify data about his/her volunteer experiences. For instance, it assists university students confirm their enrollment status into such schools together with accumulating hours served in formal volunteer programs that can be certified. This helps Non-Governmental Organizations (NGOs) by increasing their visibility hence making sure reliable existing student attendance tracking systems exist. Academic institutions possess an unparalleled capacity to manage student engagement and select partnerships simultaneously. The issuance of blockchain-secured certifications and the implementation of rigorous verification processes are both facilitated by the administrative framework, which is vital to this ecosystem. This abstract captures the essence of Harmony Hub and its potential impact on advancing a community service paradigm that is open, cooperative, and verified. What truly sets Harmony Hub apart is its novel approach to fostering collaboration between NGOs, university students, and users. This integrated platform ensures that all parties not only benefit individually but also contribute to societal betterment, a feat no other platform has previously accomplished.

Boosting the Classification of Complex Large Synoptic Survey Telescope (LSST) Data

2024-05-30T08:40:20+00:00

Analysis of light curves emanating from various celestial bodies is of paramount importance in order to enable ourselves with quantify the variability in sky and discover time-varying objects. Large Synoptic Survey Telescope (LSST)gather voluminous time-series data. However, classifying these events from large-scale surveys is a challenging task that requires efficient and robust machine learning methods. In this paper, we present a novel approach for astronomical time series classification using gradient boost, a powerful ensemble technique that combines weak learners into a strong classifier. We apply our method to two datasets from the Catalina and Zwicky TransientFacility surveys, which contain light curves of various types of transients and variables. We compare our results with state-of-the-art methods that use different features and models. We show that our method achieves superior performance in terms of accuracy with comparable computational complexity. We also discuss the advantages and limitations of our method and suggest possible directions for future work.

SecureNet:A Convergence of ML , Blockchain and Federated Learning for IoT Protection

2024-08-12T09:25:51+00:00

The Internet of Things (IoT) has become a foundational element of the digital infrastructure, extending its connectivity across various sectors and embedding intelligence in everyday devices. This article introduces SecureNet, a pioneering approach that integrates Machine Learning (ML), Blockchain, and Federated Learning (FL) to enhance IoT security. To navigate this challenging train, an innovative framework that synergizes Machine Learning (ML), Blockchain technology, and Federated Learning (FL) to fortify IoT security. SecureNet is architected to deliver a robust defense mechanism for IoT ecosystems, providing resilience against increasingly sophisticated cyber threats, and ensuring the preservation of data integrity, privacy, and unwavering system reliability. This study explores the application of advanced ML techniques NSL-KDD dataset, implementing two highly effective classifiers: Random Forest and Logistic Regression. The Random Forest classifier exhibited an exceptional accuracy of 99.85%, while the Logistic Regression model demonstrated a near-perfect accuracy of 99.03%. These compelling results highlight the efficacy of ML in identifying and mitigating activities within network traffic. SecureNet leverages ML’s profound analytical capabilities for intelligent threat discernment, Blockchain’s immutable ledgers for unassailable data verification, and FL’s privacy-centric approach to distribute model training. These outcomes underscore the potential of ML models to enhance IoT security by accurately identifying malicious patterns and anomalies within network traffic.

Heart Attack Prediction Using Federated Learning on Distributed Medical Data

2024-09-03T09:17:14+00:00

Despite advances in cardiology, heart disease continues to be a major global challenge. The development of tools for early detection and accurate prediction of the probability of triggering a heart attack as a critical event in heart disease is essential. Traditional machine learning models for heart attack prediction are a violation of medical data privacy and security as they involve a centralized dataset. Another model, federated learning, is the optimal way to keep decasualized privacy data from being available in multiple medical institutions. In this work, we conduct a study to determine how effective FL is in predicting heart attack using Logistic Regression and Support Vector Machine models with large-scale simulated distributed medical data. The first model yielded an accuracy of 88.52%, indicating that to some extent, heart attack prediction is a use case for FL. We also conduct further research on other models, and the SVM model demonstrated an accuracy of 86.89%, which is considered a fully dependent variable to be predicted as favorable. The current research also examines additional models, including K-Nearest Neighbors and Decision Tree. The latter showed lower performance, exercising an accuracy of 68.89%, while it has higher value in interpretability. It deserves to be aware that the research focus is the communication overhead within the FL framework. In my opinion, it is significant to proceed with the further investigations on the enhancements of optimum communication approaches balancing the model accuracy, training time, and communication cost. Moreover, privacy preservation within the FL deserves to be highlighted. It is worth mentioning that current research is the initial attempt, whereas privacy-preserving techniques customized for LR and SVM within the FL remain an unknown field to be analyzed. Overall, through this research, we have showed the significant potential of the FL approach for heart attack prediction with the use of distributed medical data. This future was proposed by considering the observance of privacy limitations on the accessed datasets. The FL could remain as a significant solution in the development of appropriate machine learning models, enhancing the efficiency of communication, and providing privacy considerations with an opportunity to minimize the risks of compromise.