UCP Journal of Engineering & Information Technology
type: channel
description: UCP Journal of Engineering and Information Technology is HEC recognized (Y-Category) research journal UCP Journal of Engineering and Information Technology (UCP-JEIT) is a multidisciplinary, peer-reviewed, open-access journal jointly published by the Faculty of Engineering and the Faculty of Information Technology & Computer Sciences. The journal is devoted to publishing research in Engineering, Information Technology, and Computer Sciences. ISSN: 3005-8015 (Online), 3005-8007 (Print)
publisher: University of Central Punjab
language: en
publication name: UCP Journal of Engineering & Information Technology
issn: 3005-8007
items: RUPT: An Extension to Traditional Compilers in C++ to Support Programming in Native Language
items: Smart Resource Allocation for Mobile Edge Network in IoT Using Game Theory
items: Microservice Antipatterns: Causes, Detection, and Refactoring Challenges
items: Maximum Efficiency Point Tracking Control for Dynamic Wireless Power Transfer
items: Advanced Fault Detection, Classification, and Analysis Framework for HV Transmission Lines using RT Synchronized Monitoring and Control Systems
items: Securing Financial Transactions: Leveraging Random Forest for Credit Card Fraud Detection
items: A Design-Oriented Classification of Microservice Smells
items: Health Predictions Redefined: The Impact of AI on Future Disease Diagnosis
items: Auto-Classification of FIA-Cybercrime Wing Complaints Using Bidirectional Encoder Representations from Transformers Model
items: DDoS Attack Detection System Using Machine Learning Techniques
items: Heart Attack Prediction Using Federated Learning on Distributed Medical Data
items: SecureNet:A Convergence of ML , Blockchain and Federated Learning for IoT Protection
items: Harmony Hub: Bridging Communities Through Digital Collaboration and Authentic Service Recognition
items: Boosting the Classification of Complex Large Synoptic Survey Telescope (LSST) Data
items: Micromobility in Pakistan
items: Floating Satellite with Ultrasonic Radar Payload for Debris Detection
items: Harvest Link - Blockchain in Agriculture Supply Chain
items: Studying the Invasion of Drones in Indigenous Areas Using Machine Learning Techniques
items: Evaluating the Effectiveness and Resilience of SSL/TLS, HTTPS, IPSec, SSH, and WPA/WPA2 in Safeguarding Data Transmission
items: Performance Evaluation of MANET Routing Protocols Based on Size and Speed Parameters
items: A Breakthrough of Wireless Charging Prototype
items: Database Technology for Blockchain
items: Data Models, Semantics, Query languages
items: Vehicle Safety Sensory System
items: Automatic Approach to Identify Patronizing and Condescending Language Towards Vulnerable Community

RUPT: An Extension to Traditional Compilers in C++ to Support Programming in Native Language
type: item
description: The medium of instruction has a significant impact on effective communication and comprehension. The majority of literature is available in English because it is a universal language; however, if someone is provided with the information in their native language, they will comprehend the concept much more effectively. In computer science, source code of programming languages is written in the English language, whereas endemic language has its own impact. To address this gap, this study has rendered a framework, “Roman Urdu Programming Translator” (RUPT), that will be used to translate a program coded in Roman Urdu or Hindi into a proportionate C++ program. Actually, this will be an additional layer to the original C++ compiler, i.e., a programmer will write a program using special keywords of the Roman Urdu language, use RUPT to translate it into an equivalent C++ program, and then use the traditional C++ compiler to compile and run it on the computer. The special set of Roman Urdu keywords includes, e.g., “keyboardSay (ks)” instead of “cin”, “screenKiTaraf (skt)” instead of “cout”, “klea” instead of “for” etc. In this work, additional Urdu and Hindi Roman keywords are added to the original set of C++ language and will be replaced by the C++ equivalent keywords by our translator RUPT to convert it into a pure C++ program. RUPT is only composed of the lexical analysis phase. State of the art has increased the understanding and learning rate of novel users towards the field of computer science.
subject: Articles
  topic: section
subject: C++
  topic: keywords
subject: Compiler
  topic: keywords
subject: Lexical Analysis
  topic: keywords
subject: Native Language
  topic: keywords
subject: Roman Urdu
  topic: keywords
subject: RUPT
  topic: keywords
creator: Muhammad Ishtiaq
creator: Maryam Gulzar
creator: Muhammad Farhat Ullah
rights: Copyright (c) 2025 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2025-07-23
publication date: 2025-07-23
starting page: 41
ending page: 48
doi: 10.24312/ucp-jeit.03.01.591

Smart Resource Allocation for Mobile Edge Network in IoT Using Game Theory
type: item
description: Emerging from years of research and development, the modern era of computing recognizes Internet of Things (IoT) as most empowering technology to connect the digital and real world. IoT has introduced new advancements that are transforming the world, however, it still faces constraints that limit its effectiveness in various application areas, including computing power, resource allocation, reliability, and time consumption. Achieving acceptable latency for task operations on IoT devices necessitates the appropriate allocation of Mobile Edge Caching device computing resources which should be based on task size, delivery, and service latency. It is impossible to handle the billions of data requests originating from a growing number of base stations. This research proposes a general mechanism for allocating computing resources and caching to facilitate efficient scheduling in cellular networks. A game theory approach used to model miniaturization problems has been employed in this work. A wireless network system has been analyzed where each node in the system is a participant with its strategies and contributions to achieve the desired performance. The simulation results show that the proposed technique has great potential to improve resource allocation. Each IoT device increases the number of requests handled by the MEC server in the non-cooperative subgame. The proposed system efficiently allocates IoT resources and excels in performance and reduce latency.
subject: Articles
  topic: section
subject: —: Internet of Things
  topic: keywords
subject: Game theory
  topic: keywords
subject: Resource allocation
  topic: keywords
subject: Mobile Edge Computing
  topic: keywords
creator: Samra Shereen
creator: Asif Kabir
creator: Syed Mushhad Mustuzhar Gilani
creator: Abdur Rehman Riaz
creator: Zahid Mahmood
rights: Copyright (c) 2025 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2025-07-23
publication date: 2025-07-23
starting page: 30
ending page: 40
doi: 10.24312/ucp-jeit.03.01.543

Microservice Antipatterns: Causes, Detection, and Refactoring Challenges
type: item
description: Microservice antipatterns negatively impact quality, necessitating a thorough understanding of their causes, effects, and solutions. This study provides a comprehensive review of antipatterns after analyzing 50 studies through a multivocal literature review. Key findings show that unprepared adoption and team culture are major causes, affecting maintainability, performance, and testing. Detection techniques are categorized into five groups, with most tools using search-based approaches. Four refactoring strategies were identified, along with their limitations. The study also highlights research gaps and challenges, guiding future work in improving detection and refactoring methods to mitigate antipattern effects.
subject: Articles
  topic: section
subject: Microservice Architecture
  topic: keywords
subject: Anti-patterns
  topic: keywords
subject: Antipatterns Detection
  topic: keywords
subject: Antipatterns Refactoring
  topic: keywords
creator: Junaid Aziz
creator: Ghulam Rasool
rights: Copyright (c) 2025 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2025-07-23
publication date: 2025-07-23
starting page: 17
ending page: 29
doi: 10.24312/ucp-jeit.03.01.515

Maximum Efficiency Point Tracking Control for Dynamic Wireless Power Transfer
type: item
description: Wireless power transfer has evolved as a dominant research field. Roadway-powered vehicles have poor and fluctuating efficiency due to varying coupling coefficients because of their dynamic nature. Efficiency improvement efforts become useless in the event of communication failure between the transmitter and receiver sides. This paper presents a novel algorithm based on a silent handshake between the two sides to achieve the most efficient operation with varying flux linkage. The timed sequence of operations enables the continuous load mapping by impedance matching converter to maximize the efficiency followed by load regulation done by the supply converter based on accurate mathematical modelling. Simulation and experimental results are presented to ascertain the performance of the algorithm and the mathematical models.
subject: Articles
  topic: section
subject: Dynamic coupling
  topic: keywords
subject: Impedance matching converter
  topic: keywords
subject: LCC inverter
  topic: keywords
subject: Maximum efficiency point tracking
  topic: keywords
subject: Wireless power transfer
  topic: keywords
creator: Muhammad Umer Noor
creator: Muhammad Anique Aslam
creator: Syed Abdul Rahman Kashif
rights: Copyright (c) 2025 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2025-07-23
publication date: 2025-07-23
starting page: 09
ending page: 16
doi: 10.24312/ucp-jeit.03.01.485

Advanced Fault Detection, Classification, and Analysis Framework for HV Transmission Lines using RT Synchronized Monitoring and Control Systems
type: item
description: The emergence of new technologies such as IoT, along with the merger of renewable energies, AI, smart grids, and non linear loads is enhancing the complexity of modern power systems and detecting fault as well as its correction much harder. Traditional methods suffer from inadequate speed, accuracy, less coverage, and latency that renders them highly ineffective in varying conditions. Reliable power transmission is vital for modern infrastructure, as faults on transmission lines can disrupt supply, damage equipment, and create safety risks. This paper presents a Fault Detection and Analysis System (FDAS) designed to enhance power system reliability and efficiency by enabling early fault detection, classification, and precise fault location. The FDAS system unites sensor inputs from voltage and current transformers alongside superior analysis methods for continuous fault surveillance. Proper detection of faults enables rapid identification of faulty sections thus minimizing the duration of outages and protecting equipment from damage. Fault classification defines fault categories which allows technicians to apply suitable solutions and accurate fault location enhances repair operations to decrease operational interruptions and maintenance expenses. Moreover, the FDAS system outperforms impedance-based and wave-based traditional methods through its combination of real-time acquisition and analytical algorithms and wireless monitoring which accelerates fault detection while enabling accurate corrective actions. Identifying specific fault types through fault classification provides suitable corrective solutions while exact location determination helps minimize repair time and spending related to maintenance costs.
subject: Articles
  topic: section
subject: Transmission Lines
  topic: keywords
subject: Line to Line voltage
  topic: keywords
subject: Line to ground
  topic: keywords
subject: faults
  topic: keywords
subject: overcurrent
  topic: keywords
subject: Three Phase Generator
  topic: keywords
subject: phase to ground fault
  topic: keywords
subject: phase-to-phase fault
  topic: keywords
subject: overvoltage
  topic: keywords
creator: Zeeshan Arfeen
creator: Ehtisham Arshad
creator: Raja Massod Lark
creator: Abdur Raheem
creator: Feeha Areej
creator: Ubedullah
creator: Rabia Shakoor
creator: Zain-ul-Abiden Akhtar
creator: Muhammad Rashid
creator: Tariq Bashir
rights: Copyright (c) 2025 UCP Journal of Engineering & Information Technology (HEC Recognized-Y Category) https://creativecommons.org/licenses/by/4.0
date: 2025-02-13
publication date: 2025-02-13
starting page: 41
ending page: 51
doi: 10.24312/ucp-jeit.02.02.436

Securing Financial Transactions: Leveraging Random Forest for Credit Card Fraud Detection
type: item
description: It is a crucial responsibility in the financial sector to safeguard clients and businesses against financial losses by verification of credit cards. It is demonstrated that machine learning algorithms like random forest methods can detect fraud. Multiple decision trees are used in the Random Forest to create predictions. It is very suitable for fraud detection in credit card fraud due to its prowess with high-dimensional and huge datasets. With the help of training on labelled data and performance analysis, the Random Forest algorithm can accurately detect fraudulent transactions and help decrease financial risks. Due to its features of interpretability and durability against overfitting, it is well well-suited tool for firms wanting to improve their fraud detection systems due to. With the use of the strengths of the Random Forest algorithm, accurate analysis and categorization of complex patterns and minute abnormalities present in credit card transactions has become feasible. This strategy helps in the efficient and on-time identification of potential dangers which enables proactive measures to guard against fraudulent activity and increases the accuracy of detection. With this advantage of Random forests, institutions of finance can get an in-depth analysis of the causes that impact fraud incidents and enable them to enhance and improve their security systems. Despite of shifting dynamics of transactions and fraud strategies, the model can tackle overfitting and guarantees reliable performance. Therefore, with the addition of the Random Forest algorithm into card fraud detection frameworks, enterprises acquire the ability to strengthen their defence system and enhance the security and trust of both their clients and the overall financial ecosystem.
subject: Articles
  topic: section
subject: Random Forest Algorithm
  topic: keywords
subject: Ensemble Learning
  topic: keywords
subject: Credit Card
  topic: keywords
subject: Defence Fortification
  topic: keywords
subject: Fraud
  topic: keywords
creator: Abu Bakr Qazi
creator: Arfan Ali Nagra
creator: Khola Farooq
creator: Muhammad Yousif
creator: Muhammad Haseeb Zia
rights: Copyright (c) 2025 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2025-07-23
publication date: 2025-07-23
starting page: 01
ending page: 08
doi: 10.24312/ucp-jeit.03.01.444

A Design-Oriented Classification of Microservice Smells
type: item
description: Context: Introduction of bad smells can generate negative consequences on the quality of microservices. It is essential to gather state-of-the-art knowledge on these smells and understand the challenges they present. This will benefit researchers and practitioners in mitigating the consequences of smells in microservice-based systems. Objective: The main goal of this study is to present a comprehensive catalogue of microservice smells. Method: To document the advancements and best practices in the field of microservice smells., we performed a multivocal literature review study incorporating both academic and grey literature sources. We systematically analyzed 34 studies published from the beginning of 2014 until the end of 2023 by following standard guidelines. Results: 38 bad smells in microservices are identified and cataloged in 10 different types. Conclusion: Research gaps and open challenges are highlighted in this study. This will give directions to other researchers and practitioners towards addressing challenges posed by smells in microservices.
subject: Articles
  topic: section
subject: Microservice architecture
  topic: keywords
subject: Bad smells
  topic: keywords
subject: Anti-patterns
  topic: keywords
creator: Junaid Aziz
creator: Ghulam Rasool
rights: Copyright (c) 2025 UCP Journal of Engineering & Information Technology (HEC Recognized-Y Category) https://creativecommons.org/licenses/by/4.0
date: 2025-02-13
publication date: 2025-02-13
starting page: 33
ending page: 40
doi: 10.24312/ucp-jeit.02.02.435

Health Predictions Redefined: The Impact of AI on Future Disease Diagnosis
type: item
description: Healthcare professionals often apply a one-size-fits-all approach in patient care, potentially leading to misdiagnosis, suboptimal treatments, and higher healthcare costs. Machine-learning models have garnered attention for their ability to improve diagnostic accuracy, with numerous studies focusing on machine learning applications for individual disease predictions, such as Type II diabetes, heart disease, kidney disease, and hypertension. However, limited research has tackled the combined prediction of Type I diabetes (standard cases), Type II diabetes (gestational diabetes), and cardiovascular disease, presenting a significant research gap. To address this gap, we introduce a set of benchmark corpora based on authentic patient records, targeting specific disease categories. The first contribution is a heart disease corpus containing 606 instances. The second and third contributions consist of two separate corpora, each with 849 instances: one focused on standard diabetes cases and the other on gestational diabetes cases. We evaluated these corpora with ten machine-learning algorithms and five deep-learning algorithms, rigorously comparing their performance across common metrics, including accuracy, precision, recall, and $F_{1}$-score. Our results revealed high performance across all models, with top $F_{1}$-scores of 0.785 using Random Forest, 0.790 with Gradient Boosting, and 0.994 using BiLSTM for the combined disease prediction. These findings suggest that the proposed datasets and models provide a robust foundation for accurate and scalable high-risk disease prediction, contributing a valuable, multidimensional approach to personalized patient care
subject: Articles
  topic: section
subject: Heart disease prediction Diabetes prediction
  topic: keywords
subject: Diabetes during Pregnancy
  topic: keywords
subject: Cardiovascular disease
  topic: keywords
subject: Healthcare management
  topic: keywords
creator: Iqra MUNEER
creator: Ms Sadia
creator: Muhammad kashif
rights: Copyright (c) 2025 UCP Journal of Engineering & Information Technology (HEC Recognized-Y Category) https://creativecommons.org/licenses/by/4.0
date: 2025-02-13
publication date: 2025-02-13
starting page: 24
ending page: 32
doi: 10.24312/ucp-jeit.02.02.411

Auto-Classification of FIA-Cybercrime Wing Complaints Using Bidirectional Encoder Representations from Transformers Model
type: item
description: The Cybercrime Wing (CCW) of the Federal Investigation Agency, which was formerly known as the National Response Center for Cybercrime (NR3C), is governed by rules that were created in 2016 as part of the Prevention of Electronic Crimes Act (PECA) to combat cybercrime. Criminal activities executed using computers and the internet are referred to as cybercrimes. In order to carry out illegal activities, cybercriminals make use of any information system as their primary means of communication with the devices that belong to their victims. This research mainly focusses on the cybercrime complaints with an automated classification system. To achieve the automatic modelling for classification of different types of cybercrimes, this study used the Bidirectional Encoder Representations from Transformers (BERT). Its obstacles mainly include the possibility of human errors as it manually classifies cybercrime complaints, also that there might be delays during handling in comparison with an automated system. The dataset includes complaints submitted in English during a two years window, and it was encoded, tokenized and cleaned thoroughly. The purpose was to simplify the training process for the model. The study used a lightly fine-tuned, pretrained (BERT)-base-uncased model. The findings confirm that the model can be used for classifying complaints and exhibits an excellent classification accuracy, precision and F1-scores between different cybercrime offences indicating its supremacy among advanced Natural language processing (NLP) techniques to strengthen cybersecurity measures.
subject: Articles
  topic: section
subject: FIA
  topic: keywords
subject: CCW
  topic: keywords
subject: BERT
  topic: keywords
creator: Hafiz Ammar Mazhar
creator: Umair Altaf
creator: Syed Muhammad Anwar
creator: Muhammad Shoaib Bhutta
rights: Copyright (c) 2025 UCP Journal of Engineering & Information Technology (HEC Recognized-Y Category) https://creativecommons.org/licenses/by/4.0
date: 2025-02-13
publication date: 2025-02-13
starting page: 01
ending page: 11
doi: 10.24312/ucp-jeit.02.02.393

DDoS Attack Detection System Using Machine Learning Techniques
type: item
description: Distributed Denial of Service (DDoS) attacks are the major issues that introduce disruption of accessibility and reliability of the network services. The purpose of this paper is to demonstrate an overall recognized machine learning based system that can efficiently identify and classify DDoS attacks using a rich dataset allowing us to work with various installation network traffic attributes, we have developed an automatically classification pipeline the Random Forest Classifier which is known for its high performance in handling large datasets and heterogeneous data. These learnt models were then combined with Decision Tree, Gradient Boosting, and Logistic Regression models to provide a better way analyze the product. An important step involved in this framework is the data preprocessing pipeline that involves one-hot encoding of categorical features to numerical features and scaling for the numerical features, leading to model input optimization. Efficiency of our models is assessed through metrics just as accuracy, precision, recall, and F1-score and it is further validated using cross-validation techniques. The top models are being evaluated by powerful tools, which include feature importance visualization, confusion matrices, precision-recall curves, and calibration curves, for a deeper understanding of their predictive ability as well as their decision-making processes, within those models. A feedback loop mechanism for the iterative betterment and adaptation of the model is accounted which learns from new patterns actively. This approach demonstrates good evaluation and robust in identifying DDoS attacks that are threat of cybersecurity defenses using machine learning.
subject: Articles
  topic: section
subject: IoT
  topic: keywords
subject: DDOS
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Attacks
  topic: keywords
subject: Decision Tree
  topic: keywords
subject: Gradient Boosting Classifier
  topic: keywords
subject: Logistic Regression
  topic: keywords
subject: Feedback Loop system
  topic: keywords
subject: Network traffic
  topic: keywords
subject: DDoS Attack
  topic: keywords
subject: Network Security
  topic: keywords
subject: Cross Validation
  topic: keywords
creator: Muhammad Zunnurain Hussain
creator: Muhammad Zulkifl Hasan
creator: Khawaja Qasim Maqbool
creator: Aisha Nazir
creator: Hafiz Muhammad Furqan Farid
rights: Copyright (c) 2025 UCP Journal of Engineering & Information Technology (HEC Recognized-Y Category) https://creativecommons.org/licenses/by/4.0
date: 2025-02-13
publication date: 2025-02-13
starting page: 12
ending page: 23
doi: 10.24312/ucp-jeit.02.02.395

Heart Attack Prediction Using Federated Learning on Distributed Medical Data
type: item
description: 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 …
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Heart Attack Prediction
  topic: keywords
subject: Distributed Medical Data
  topic: keywords
subject: Privacy-Preserving Machine Learning
  topic: keywords
subject: Predictive Healthcare Analytics Introduction
  topic: keywords
creator: Rana Abdul Ahad
creator: Muhammad Zunnurain Hussain
creator: Muhammad Hassan Qamar
creator: Muzammil Mustafa
creator: Basit Sattar
creator: Jibran Ali
creator: Jawad Altaf
rights: Copyright (c) 2024 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2024-09-20
publication date: 2024-09-20
starting page: 36
ending page: 44
doi: 10.24312/ucp-jeit.02.01.326

SecureNet:A Convergence of ML , Blockchain and Federated Learning for IoT Protection
type: item
description: 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.
subject: Articles
  topic: section
subject: Internet of Things (IOT)
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Blockchain
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Data Integrity
  topic: keywords
subject: Network Traffic Analysis
  topic: keywords
creator: Abeera Malik
creator: Talha
creator: Muhammad Zunnurain Hussain
creator: Muzammil Mustafa
creator: Basit Sattar
creator: Jibran Ali
creator: Jawad Altaf
rights: Copyright (c) 2024 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2024-09-20
publication date: 2024-09-20
starting page: 24
ending page: 35
doi: 10.24312/ucp-jeit.02.01.325

Harmony Hub: Bridging Communities Through Digital Collaboration and Authentic Service Recognition
type: item
description: "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.
subject: Articles
  topic: section
subject: Collaborative volunteer platform
  topic: keywords
subject: Community service engagement
  topic: keywords
subject: Student volunteer opportunities
  topic: keywords
subject: Trustworthy volunteer ecosystem
  topic: keywords
subject: University-NGO partnerships
  topic: keywords
creator: Sheraz Tariq
creator: Muhammad Sauood
creator: Atif Nadeem
rights: Copyright (c) 2024 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2024-09-20
publication date: 2024-09-20
starting page: 06
ending page: 17
doi: 10.24312/ucp-jeit.02.01.246

Boosting the Classification of Complex Large Synoptic Survey Telescope (LSST) Data
type: item
description: 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.
subject: Articles
  topic: section
subject: LSST Dataset
  topic: keywords
subject: Light Curves
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Gradient Boosting
  topic: keywords
creator: Rao Farhat Masood
creator: Imtiaz Ahmad Taj
rights: Copyright (c) 2024 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2024-09-20
publication date: 2024-09-20
starting page: 18
ending page: 23
doi: 10.24312/ucp-jeit.02.01.270

Micromobility in Pakistan
type: item
description: Micromobility is actively being adopted in many cities around the globe. The various personal travel options included in micromobility can contribute to lower congestion, reduced greenhouse gas emissions, and improvement in the general well-being of its users. However, travel options falling under micromobility are not very common in Pakistan. This paper explores the several challenges in way of providing a supportive micromobility ecosystem in Pakistan and discusses the steps to be taken for its adoption. It examines the current infrastructure, cultural norms, and government policies present in the country and identifies gaps and areas of improvements for successful integration of micromobility. The introduction of micromobility in Pakistan presents a significant opportunity to promote sustainable and equitable transportation in the country. With the right interventions and policy solutions, Pakistan can create a transportation system that is accessible, safe, and efficient for all its citizens, and pave the way for a more sustainable future.
subject: Articles
  topic: section
subject: Bicycle
  topic: keywords
subject: E-Scooter
  topic: keywords
subject: Micromobility
  topic: keywords
subject: Pakistan
  topic: keywords
creator: Muhammad Awais Shafique
rights: Copyright (c) 2024 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2024-03-18
publication date: 2024-03-18
starting page: 24
ending page: 27
doi: 10.24312/ucp-jeit.01.02.257

Floating Satellite with Ultrasonic Radar Payload for Debris Detection
type: item
description: 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.
subject: Articles
  topic: section
subject: Satellite subsystem
  topic: keywords
subject: RODOS
  topic: keywords
subject: Aerospace
  topic: keywords
subject: Satellite mission
  topic: keywords
subject: Satellite test, Evaluation
  topic: keywords
subject: Payload integration
  topic: keywords
creator: Khubaib Ahmad
creator: Kamran Saleem
creator: Sergio Montenegro
creator: Faisal Muhammad
creator: Awais A. Khan
rights: Copyright (c) 2024 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2024-09-20
publication date: 2024-09-20
starting page: 01
ending page: 05
doi: 10.24312/ucp-jeit.02.01.183

Harvest Link - Blockchain in Agriculture Supply Chain
type: item
description: The Agricultural supply chain (ASC) is the backbone of economies around the world, playing a vital role in driving numerous industries and serving as a major stakeholder. The involvement of diverse stakeholders, including farmers, distributors, retailers, and consumers, contributes to the inherent complexity and challenges in managing the supply chain effectively. However, the lack of transparency within the supply chain not only presents challenges but also leads to inefficiencies, wastage, and susceptibility to fraudulent practices. Recognizing these deficiencies, this paper aims to comprehensively address and rectify the shortcomings in the agricultural supply chain. The significance of these issues cannot be overstated, as they have a profound impact on food security, fair trade, and the livelihoods of countless stakeholders. In response to the growing demand for transparency in the sourcing of food products, this paper endeavors to shed light on these critical aspects, fostering improved trust, enhanced accountability, and the assurance of safety and quality in food products for consumers. To effectively tackle the identified deficiencies, the paper proposes the utilization of Blockchain technology as the primary mechanism to establish an immutable ledger for every transaction and product movement, ensuring data integrity and trust without the need for intermediaries. Each stage of the supply chain, from cultivation to distribution, will be recorded in a tamper-proof manner, allowing consumers and stakeholders to effortlessly trace the history of agricultural products. Additionally, the integration of AI is proposed to analyze blockchain data, providing real-time insights and predictions to further enhance transparency. While acknowledging potential barriers such as technical challenges, education, and government policies, this fusion of blockchain and AI stands as a promising technology to create a robust, transparent, and accountable agricultural supply chain…
subject: Articles
  topic: section
subject: Agriculture Supply Chain
  topic: keywords
subject: Transparency
  topic: keywords
subject: Blockchain Technology
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Food Security
  topic: keywords
subject: Fair Trade
  topic: keywords
subject: Data Integrity
  topic: keywords
subject: Trust, Accountability
  topic: keywords
subject: Real-time Insights
  topic: keywords
subject: Predictive Analysis
  topic: keywords
subject: Technical Barriers
  topic: keywords
subject: Education
  topic: keywords
subject: Regulatory Frameworks
  topic: keywords
creator: Usman Muhammad Jahangir Alvi
creator: Syed Daniyal Ahmed
creator: Muhammad Sauood
rights: Copyright (c) 2024 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2024-03-18
publication date: 2024-03-18
starting page: 28
ending page: 35
doi: 10.24312/ucp-jeit.01.02.258

Studying the Invasion of Drones in Indigenous Areas Using Machine Learning Techniques
type: item
description: The prevalence of drones in contemporary times has become widespread, representing a pivotal technological advancement in aviation—characterized by its autonomous, pilot-less nature. However, its predominant application for surveillance and targeted operations, particularly by the United States of America (USA), has sparked vehement criticism due to perceived violations of human rights on a global scale. Especially, Pakistan-like countries have borne the brunt of drone strikes, with the Federally Administered Tribal Areas being the primary target, accounting for over 90% of these attacks. This research delves into the profound impact of drone strikes, focusing on the often-overlooked innocent victims, including women and children, as well as the consequential damage to the affected regions. In this paper, we posit that a classification-based approach offers a more comprehensive and statistically informative means of elucidating patterns inherent in the data. By doing so, we aim to shed light on the effectiveness of targeted killings in the context of counter-terrorism. The proposed approach includes machine learning algorithms, such as ZeroR, J48, Naive Bayes, and OneR that have been employed to meticulously analyze the dataset and unveil hidden patterns. In particular, the J48 algorithm demonstrated exceptional performance, accurately discerning casualties within the standard Kaggle noisy dataset. The Weka tool, known for its advanced capabilities, played a pivotal role in this analysis, handling crucial tasks such as initial pre-processing, numeric to nominal conversion, and replacing missing values. This integrated approach ensures a robust exploration of the dataset, leveraging the strengths of diverse algorithms and sophisticated tools for comprehensive insights. This departure from traditional legal analyses broadens the discourse surrounding drone warfare, emphasizing the importance of data-driven insights in understanding the broader implications of these op…
subject: Articles
  topic: section
subject: Drone attack
  topic: keywords
subject: civilian
  topic: keywords
subject: Data Mining
  topic: keywords
subject: Pattern Analysis
  topic: keywords
creator: Samra Nawazish
creator: Amin Ullah
creator: Jawad Hassan
creator: Saddam Hussain Khan
creator: Abdul Aziz
creator: Riaz Ud Din
creator: Muhammad Adeel
rights: Copyright (c) 2024 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2024-03-18
publication date: 2024-03-18
starting page: 17
ending page: 23
doi: 10.24312/ucp-jeit.01.02.230

Evaluating the Effectiveness and Resilience of SSL/TLS, HTTPS, IPSec, SSH, and WPA/WPA2 in Safeguarding Data Transmission
type: item
description: This research paper focuses on the analysis of network security protocols, which are essential for protecting the confidentiality, integrity, and availability of data transmitting over the network. More and more communication protocols developed over the period to provide reliable and effective communication, and for most of them, security was their significant goal. The paper covers a comprehensive overview of different security protocols such as SSL/TLS, HTTPS, IPSec, SSH, and WPA/WPA2, including their working principles and strengths. The study also analyzes the current emerging threats and attacks on the current state of network security and how well the analyzed protocols function to mitigate them. The finding of the paper helps network administrators to ensure the highest level of security for their networks.
subject: Articles
  topic: section
subject: Network Security Protocols
  topic: keywords
subject: Data Transmission
  topic: keywords
subject: HTTPS
  topic: keywords
subject: IPSec
  topic: keywords
subject: SSH
  topic: keywords
subject: WPA/WPA2
  topic: keywords
subject: Emerging Threats
  topic: keywords
subject: SSL/TLS
  topic: keywords
creator: Zain Tariq
creator: Bint e Zainab
creator: Muhammad Zunnurain Hussain
rights: Copyright (c) 2024 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2024-03-18
publication date: 2024-03-18
starting page: 01
ending page: 07
doi: 10.24312/ucp-jeit.01.02.144

Performance Evaluation of MANET Routing Protocols Based on Size and Speed Parameters
type: item
description: Mobile nodes in Mobile Ad Hoc Networks (MANETs) play a dual role, functioning both as routers and hosts. This dual functionality enables them to receive incoming traffic and direct outgoing traffic. Energy efficiency stands as a critical factor when it comes to routing protocols. It plays a pivotal role in conserving the battery life of nodes involved in routing network traffic. In the scope of this research, we evaluated three routing protocols: TORA, OLSR, and GRP. Our analysis focused on their performance across various key metrics, including throughput, network end-to-end delay, and network load. To validate our findings, we conducted simulations within an office network environment measuring 1000m x 1000m, leveraging the OPNET Modeler 14.5 network simulation tool. The results of our simulations revealed that the OLSR protocol demonstrated the highest network load among the three protocols. In terms of network end-to-end delay and throughput, the OLSR protocol demonstrated superior performance compared to both the GRP and TORA protocols. Conversely, when it comes to network load, the TORA protocol outperformed both the GRP and OLSR protocols. On the other hand, the GRP protocol excelled in comparison to the TORA protocol when considering network end-to-end delay and network throughput.
subject: Articles
  topic: section
subject: MANETs
  topic: keywords
subject: TORA
  topic: keywords
subject: OLSR
  topic: keywords
subject: GRP
  topic: keywords
subject: Routing-Protocols
  topic: keywords
subject: OPNET
  topic: keywords
creator: Amin Ullah
creator: Samra Nawazish
creator: Jawad Hassan
creator: Saddam Hussain Khan
creator: Danish Arif
creator: Muhammad Usama Sharaf
rights: Copyright (c) 2024 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2024-03-18
publication date: 2024-03-18
starting page: 08
ending page: 16
doi: 10.24312/ucp-jeit.01.02.217

A Breakthrough of Wireless Charging Prototype
type: item
description: In this modern era, every mankind requires a system that can transfer power in a very efficient and safe way. These research fields have become one of the researches’ to be studied and considered for implementation into modern technologies. The circuit of the two individual coils that are used and simulated to get the best power transfer efficiency using the specialized software, MATLAB Simulink. The inductive power transfer technique is applied to transfer energy from the transmitter to the receiver through the air gap at an optimum distance to get the highest efficiency possible. By using MATLAB software, the effects of the parameters such as the inductance, resistance, capacitor, and mutual inductance value can be altered to be studied in detail to provide the efficiency goal needed. The relevant parameters that can provide the best efficiency towards the wireless power transfer can also be determined. Finally, the charger efficiency for charging the battery wirelessly can be determined to be charged at a lesser time without wasting the power source energy. The obtained knowledge from the researchers is used to reveal the design which is optimum for power transfer for the wireless charging prototype.
subject: Articles
  topic: section
subject: Wireless Charging
  topic: keywords
subject: Inductive Power
  topic: keywords
subject: Coil
  topic: keywords
subject: Air Gap
  topic: keywords
subject: Efficiency
  topic: keywords
creator: NORAMALINA
rights: Copyright (c) 2023 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2023-10-30
publication date: 2023-10-30
starting page: 22
ending page: 31
doi: 10.24312/ucp-jeit.01.01.169

Database Technology for Blockchain
type: item
description: This paper provides an overview of blockchain technology, including its unique properties such as its distributed nature, immutability, and consensus mechanisms. The paper examines the history of blockchain, its cryptographic protocols, smart contracts, and consensus mechanisms, and explores potential use cases in various industries. The authors also compare traditional databases to blockchain technology and propose a common blockchain-based database for intrusion detection systems. The report highlights the potential benefits of this proposed system, including data integrity, security, and fault tolerance. The authors conclude by emphasizing the importance of trust and transparency in blockchain technology, and how it can be applied beyond cryptocurrency to areas such as traceability of medical data, interoperability, and insurance business deals.
subject: Articles
  topic: section
subject: blockchain
  topic: keywords
subject: blockchain technology
  topic: keywords
subject: privacy
  topic: keywords
subject: cryptocurrency
  topic: keywords
subject: bitcoin
  topic: keywords
subject: decentralization
  topic: keywords
subject: cryptography
  topic: keywords
subject: databases
  topic: keywords
creator: Ahmed Nasir Khan
creator: Muhammad Zunnurain Hussain
creator: Summaira Nosheen
creator: Qasim Hussain
rights: Copyright (c) 2023 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2023-10-30
publication date: 2023-10-30
starting page: 32
ending page: 38
doi: 10.24312/ucp-jeit.01.01.206

Data Models, Semantics, Query languages
type: item
description: This study tries to offer a complete review of database concepts, semantics, and query languages. A data model is a theoretical data representation that specifies the structure, connections, and restrictions of data in a certain area. It allows you to organise and categorise data for easier organisation, access, and management. A query language is a kind of computer programming language that is used to get information from a database. It enables users to describe the data they wish to obtain as well as the criteria under which the data must be provided. In the overall context of data modelling and query languages, semantics refers to the interpretation and significance of data and questions. It entails setting the rules and limitations for data storage, retrieval, and manipulation, as well as verifying that query results are consistent and relevant. Data modelling, query language, and semantics work together to create the basis of database management systems, allowing for efficient and effective data storage and retrieval for a broad variety of applications. We intend to give a greater knowledge of these ideas and their usefulness in data management by exploring the strengths and shortcomings of various data models and query languages, as well as highlighting the problems and applications of semantics. Here, also determines the expansion for the global usage of EHR systems and the demand for efficient data management services which built for the betterment of health records. A precise literature study was carried out, encompassing a wide variety of publications and themes such as Data Models, Semantics, Query Languages, the importance of these ideas in database systems, and an assessment of the advantages and drawbacks of numerous data models.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: data structures, semantics, query languages, database systems, data models, programming languages, strengths, drawbacks, syntax, benefits, contrast, analyses, seman- tics, practice, electronic health records
creator: Rameesha Ahmad
creator: Muhammad Zunnurain Hussain
creator: Muhammad Zulkifl Hasan
creator: Muhammad Abubakar Afaq
rights: Copyright (c) 2023 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2023-10-30
publication date: 2023-10-30
starting page: 08
ending page: 19
doi: 10.24312/ucp-jeit.01.01.165

Vehicle Safety Sensory System
type: item
description: Our daily lives depend on vehicles for transportation, work, and adventure. Traffic accidents, a global issue, harm individuals, communities, and societies. Accidents often come from human errors, diversions, and judgment. This report underlines highway safety accidents' terrible consequences, from death to economic expenses. Many variables make accidents inevitable, but preventing them is crucial. Accidents are caused by human error, particularly distracted and inebriated driving. This study emphasizes driver status detecting systems' road safety benefits. These technologies evaluate drivers' safety via real-time monitoring of attentiveness, weariness, and impairment. Driver fatigue, alcohol or drug impairment, and cognitive distractions enhance accident risk. Eye tracking and facial recognition offer real-time solutions for these factors. Driver status detection devices warn drivers, preventing accidents. Tired or distracted driving can cause serious accidents. The adoption of driver status detection systems is vital. Prevention is possible with these tools that detect weariness and distraction. Addressing these concerns may reduce accidents and save lives. According to this study, the Vehicle Sensory Safety System actively analyzes drivers' physiological states using BPM and grip pressure. These dynamic data reveal the driver's emotional and physical state in real-time. The technology alerts drivers with a buzzer, vibrating steering wheel, and flashing LED strip. This project could improve road safety for all users and reduce highway accidents.
subject: Articles
  topic: section
subject: Driver monitoring system
  topic: keywords
subject: Heart rate
  topic: keywords
subject: Accident prevention
  topic: keywords
subject: Internet of things (IoT)
  topic: keywords
subject: Sensor
  topic: keywords
creator: NORAMALINA
creator: Cui Chengjun
rights: Copyright (c) 2023 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2023-10-30
publication date: 2023-10-30
starting page: 20
ending page: 26
doi: 10.24312/ucp-jeit.01.01.168

Automatic Approach to Identify Patronizing and Condescending Language Towards Vulnerable Community
type: item
description: In this study, we introduce a newly clarified dataset that is intended to help NLP algorithms identify and categorize language that is patronizing and condescending to weak communities (for example homeless peoples, Refugees, Poor families). While the use of such language in the general media has long been shown to have negative effects, it differs from other forms of damaging language in that it is frequently used unintentionally and with good intentions. We also believe that the sometimes-covert character of insulting and demeaning language poses an intriguing professional challenge for the NLP community. Our analysis of the presented dataset reveals that typical NLP models, with language models, have trouble recognizing PCL (Patronizing and Condescending Language). Also work on the research that are investigate the existing accuracy of patronizing and condescending language, accuracy of a huge dataset be compared to the methods already in use and he patronizing accuracy of language of vast datasets be improved? We furthermore believe that the often-subtle nature of patronizing and condescending language (PCL) presents an interesting technical challenge for the NLP community. Our analysis of the proposed dataset shows that identifying PCL is hard for standard NLP models, with language models such as BERT achieving the best results
subject: Articles
  topic: section
subject: Natural language process (NLP)
  topic: keywords
subject: Bidirectional Encoder Representation from transforms (BERT) Approach
  topic: keywords
subject: Prediction
  topic: keywords
subject: Patronizing words
  topic: keywords
creator: Mehmood Anwar
creator: Syeda Mehak Kazmi
creator: Abrar Ahmed
creator: Muhammad Ashir
rights: Copyright (c) 2023 UCP Journal of Engineering & Information Technology https://creativecommons.org/licenses/by/4.0
date: 2023-10-30
publication date: 2023-10-30
starting page: 1
ending page: 7
doi: 10.24312/ucp-jeit.01.01.136