Department of Computer Science

Permanent URI for this communityhttps://repository.cuilahore.edu.pk/handle/123456789/16

Browse

Author: search.filters.author.Dr. Farooq AhmadSubject: search.filters.subject.Computer Science

Search Results

Now showing 1 - 5 of 5
  • No Thumbnail Available
    Item
    Formal Modeling and Analysis of Distributed Measurement Systems in Cyber-Physical Systems Using Colored Petri Nets
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Aqsa Nawaz; CIIT/FA23-RCS-002/LHR; Dr. Farooq Ahmad; LHR TP 10033
    This research addresses the reliability and consistency challenges associated with the Cyber- Physical Systems (CPS) driven Distributed Measurement Systems (DMS), which capture, collect and process DMS data or information from measurement nodes located in various geo- graphical areas. Due to being distributed and asynchronous, these systems suffer from causal inconsistencies and synchronization delays, and unstable scheduling, thereby undermining dependency in fields like power management systems and smart grids. The first stage of this approach aims to obtain a formal specification of the DMS behaviour using Computation Tree Logic (CTL) and Linear Temporal Logic (LTL). These formal languages clearly capture safety and liveness properties like the absence of deadlock, safe power switching, reliable communication, safe and correct data processing, etc. The second stage of the approach is to build a system under study, a hierarchical Coloured Petri Net (CPN) model in CPN Tools1 using well-defined ColSets, functions, transitions, guards, and inter-page communication to simulate measurement power behaviour and control switching with respect to scheduling, measurement flow, and faults. An executable and semantically rich operational model of the system is obtained from this model. The final step validates the value of the CPN model with a detailed state-space analysis. State- space properties, such as boundedness, liveness, fairness, home-state behaviour, and Strongly Connected Component (SCC), are computed and compared to formal specifications. The analysis outcomes demonstrate that the modelled system fulfils all formalisms and performs acceptably from the system operational viewpoint within the analysed state space
  • No Thumbnail Available
    Item
    Histone Explorer: AI-Enhanced Identification and Characterization of Histone Modification
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Umar Farooq; CIIT/SP24-RCS-017/LHR; Dr. Farooq Ahmad; LHR TP 10039
    Histone modifications are one of the key epigenetic mechanisms to control the structure of chromatids and gene expression and play a pivotal role in cellular differentiation, development, and pathogenesis. In spite of the fact that Chromatin Immunoprecipitation Sequencing (ChIP-seq) is an experimental methodology that allows identifying histone marks through the use of high-resolution sequencing, it is expensive, labor-intensive and has low scalability, preventing its general use in a wide range of biological studies. To overcome these difficulties, this research suggests to consider Histone Explorer as an AI-enhanced computational system of the correct recognition and description of sites of histone modifications directly on the basis of genomic sequence information. The suggested framework will consist of a bioinformatics preprocessing pipeline that is reproducible, and sophisticated artificial intelligence models. Peaks of histone modification genomic intervals were obtained at the public repositories of epigenomic repositories such as ENCODE and Roadmap Epigenomics and mapped to a reference genome, hg38, to produce a curated FASTA sequence dataset. An extensive feature extraction scheme was used in order to obtain local and global sequence features, such as nucleotide frequency patterns, k-mer representations, positional features, pairwise interaction features, and higher-order statistical moments. It was trained and tested on the following models: CNN, BiLSTM, GRU, and attention- based transformer models among various models trained using machine learning, deep learning, and transformer-based architectures. There were 100,000 positive and negative samples of Histone sequences in the dataset. The performance of the models was strictly tested with independent test sets and using the k-fold cross-validation protocols. The experimental findings show that the transformer-based Histone Explorer model performs better than both classical and deep learning baselines with an accuracy of 91.5, precision of 90.2, recall of 92.8 and F1-score of 91.5 and AUC- ROC of 95.0. Such findings demonstrate that there is high generalization properties and strength over a wide spectrum of genomic regions. All in all, Histone Explorer offers a scalable, precise, and biologically meaningful AI-based framework of histone modifications identification. The proposed system can provide a useful computational resource in epigenetics studies, biomarker discovery, and precision-medicine studies because it lowers reliance on expensive experimental studies and allows analyzing epigenomics on a high-throughput.
  • No Thumbnail Available
    Item
    Data Driven Prediction of Student Performance: Integrating Academic, Social, Personal and Behavioral Factors in Pakistan Higher Education
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Frazeen Babar; FA22-RCS-026; LHR TP 9490; Dr. Farooq Ahmad
    Student performance is an important aspect of any educational institution. Several factors affect a student’s performance, e.g., learning assessments, co-curricular activities, previous achievements, personal and social Interactions, etc. Currently, in Pakistan, there is a lack of any such system that helps to analyze and predict student performance. There are two primary reasons for this, 1) lack of research into existing prediction methods to determine the best suitable approach for evaluating student performance in Pakistani institutions, 2) limited research on the factors that influence specifically Pakistani students' performance. The essential goal of this research is to foster predictive models for GPA evaluation using machine learning (ML) and deep learning (DL) techniques. The main research problems explore the efficiency of various features and algorithms in predicting students' cumulative GPAs (CGPA) and pass/fail outcomes. To close this gap, we first gather a sizable dataset of several characteristics that influence a student's academic success in this study project. The COMSATS University, Lahore Campus students studying computer science and software engineering are the source of the data. Additionally, we assessed the suggested dataset using (ML & DL) models to forecast students' performance in terms of regression (CGPA) and classification (pass/fail). The assessment is conducted utilizing f-score, accuracy, precision, and recall. The examination of RMSE, R2 , and MAE is used to assess the performance of regression techniques. The results of this study might be advantageous to academic institutions, teachers, and students. The study concludes by promoting the use of predictive models to backing academic interferences and institutional planning, with the potential for wider approval in several educational situations
  • No Thumbnail Available
    Item
    Automatic Code Generation of Petri Net-Based Business Process Models
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Rana Marwat Hussain; SP19-RCS-021; LHR TP 7292; Dr. Farooq Ahmad
    To verify software system flows and different components before the implementation of the system formal modeling techniques become very useful. Modeling helps users to understand software systems better by defining the system’s components formally. Many mathematical modeling languages are used for this purpose but the widely used language for formal modeling is Petri Nets. Traditionally all the efforts involved in the modeling phase is nothing more than to support the documentation because the development process manual transfer the modeling into implementation which because information lost is the real system implementation (program Code Generation). To reduce the implementation and modeling gap Model-Driven Software Engineering (MDSE) plays a very crucial role as MDSE approaches favor code generation from models which helps to improve productivity, quality of the application and reduce the time spent on implementation. As code generation is the fundamental part of the MDSE and model play a key role that is why tool support proposed in this thesis work tool provides a modeling interface and an approach to generate code from Petri net in multiple high-level programming languages such as Python, C, C++, Java, etc. Through the use of modeling interface, four models have created these models were used to generate code and the tool successfully generated code in multiple high-level programming languages which can be compiled and run-on supportive IDEs. However, the proposed tool had flaws in the modeling such as the CTRL+ key sometimes misses the draw arc operation, and code generated from complex models has logical flaws. The proposed tool can be useful in future research work for providing support to extend the code generation approach, and modeling interface to modeled other domain’s model which based on another formal modeling also to support the other informal modeling languages such as UML class diagram, etc. and help to generate code
  • No Thumbnail Available
    Item
    Formal Modeling and Analysis of Security Schemes in Internet of Things
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Muhammad Amar Sohail; FA16-RCS-020; LHR TP 5971; Dr. Farooq Ahmad
    The Internet of Things (IoT) has provided us with the notion of a smartly connected world. Internet of Things is a system in which gadgets, computing machines or mechanical devices, are connected with each other through Unique IDs. Those objects can accumulate and disseminate the packets from one node to other nodes without any human participation. As IoT is related to the network-based distributed systems such as i.e. Cloud Computing, Fog Computing, and Body Area Network etc. These networks when applied to sensitive and confidential infrastructure then the role of its security becomes very crucial. To this end, routing strategy is responsible and literature suggests different techniques for security and privacy in IoT Systems. Formal modeling of a system can help us to check the reliability, accuracy and consistency of the system. Colored Petri Nets is graphic-based language and it has taken premium space in design and specification, validation and verification of systems which are concurrent in its behavior. Security Scheme is the mechanism of securing a network system by setting up routing strategy through topology. Analysing the attacks and mitigating them is a small subset of security schemes. In this study Routing over Low Power and Lossy networks (RPL) is used as a case study and the aim was to present the idea of translation of into formal language and ultimately analyze its security with the means of Formal Specification Methods. RPL is an IPv6 protocol and prominent for the routing in Internet of Things. Eventually, the focus will be shifted towards the evaluation of the existing security standards for RPL through formal validation and verification to check if those standards can achieve the desired security level.