MS: Recent submissions

  • Adaptive Network Intrusion Detection System (NIDS) Using Deep Learning (DL) 
    Saeed, Haroon (College of Electrical & Mechanical Engineering (CEME), NUST, 2025)
    The rising sophistication of cyber threats necessitates the prompt adoption of sophisticated and accurate Network Intrusion Detection Systems (NIDS) to safeguard digital assets. However, many challenges are faced in the ...
  • Enhancing Dynamic Connectivity: Employing Machine Learning for Cognitive Link Adaptation in Narrowband and Wideband Networks 
    Ismail, Fatima (College of Electrical & Mechanical Engineering (CEME), NUST, 2025)
    The demand for faster data transfer rates rises along with the number of mobile devices, and the radio spectrum becomes more crowded. The forthcoming 5G wireless commu nication technology seeks to significantly enhance ...
  • V-CAS: A Realtime Vehicle Collision Avoidance System Using Deep Learning on Multiple Camera Streams 
    Ashraf, Muhammad Waqas (College of Electrical & Mechanical Engineering (CEME), NUST, 2025-01)
    This research introduces a robust real-time Vehicle Collision Avoidance System (V-CAS) aimed at enhancing vehicle safety through environmental perception-based adaptive braking. V-CAS utilizes the advanced vision-based ...
  • Transformer based Interpretable Model for Semantic Segmentation of Skin Histology Images 
    Fatima, Sana (College of Electrical & Mechanical Engineering (CEME), NUST, 2024-12)
    This study aims to enhance the generalizability and robustness of the deep learning transformer algorithm employed in segmenting skin cancers and tissues using incremental learning. While deep learning models frequently ...
  • Target Tracking in Wireless Sensor Network 
    Umair Shafiq Khan (College of Electrical and Mechanical Engineering (CEME), NUST, 2017)
    With increasing advancement and popularity of wireless sensor networks, their applications in different areas are becoming more and more sophisticated. As wireless sensor networks and its components are distributed in ...
  • Motor Imagery EEG Signal Classification Using Deep Learning Approach 
    Ahsan, Muhammad (College of Electrical & Mechanical Engineering (CEME), NUST, 2024-10-28)
    Motor Imagery electroencephalography (MI EEG) data are employed in brain computer interface (BCI) systems to identify the intention of participants. Several factors, such as poor signal to noise ratios and a scarcity of ...
  • Diagnosis And Prognosis Of COVID-19 Using Volumetric Three-Dimensional Computed Tomography Images 
    Khan, Mohsin Ali (College of Electrical & Mechanical Engineering (CEME), NUST, 2024)
    This research presents two key contributions aimed at improving COVID-19 severity prediction, specifically intubation or death within one month using 3D CT scan data. First, we introduce a novel dataset of 2,000 segmented ...
  • Segmentation and Classification of Gastroenterology Images using Saliency Mapping in CNNs 
    Khan, Yusra (College of Electrical & Mechanical Engineering (CEME), NUST, 2024-08-27)
    This research work has been done towards the treatment of Gastrointestinal (GI) Cancer by experimenting as to how a computer aided design can help oncologists classify and segment GI Cancer using Gastroenterology (GE) ...
  • Identification, Segmentation and Localization of GI Polyps in Colonoscopy Using Deep Learning Techniques 
    Shakeel, Abdusamad (College of Electrical & Mechanical Engineering (CEME), NUST, 2024-08-07)
    Computers have improved the old colonoscopy procedures as they are helpful in the early detection of the problem and help in pin-pointing the issue by highlighting the main areas of the interest. With the improvement in ...
  • Enhancing Flood Monitoring and Prevention Using Machine Learning and IoT Integration 
    Bukhari, Syed Asad Shabbir (College of Electrical & Mechanical Engineering (CEME), NUST, 2024-06)
    Floods require good monitoring and preventive measures; they pose risk factors to humankind, property, and infrastructure. This thesis proposes a new approach to enhancing the integration of Internet of Things (IoT) ...
  • Specific Emitter Identification using Deep learning Models 
    Montaha, Muhammad (College of Electrical & Mechanical Engineering (CEME), NUST, 2024-04)
    In today’s world, Specific emitter identification (SEI) has become crucial task in elec tronic warfare and signal intelligence that involves identifying a particular communication device by examining the unique radio ...
  • Spinal Imaging Data Analysis & Clinical Parameter Extraction Using Transformers 
    Liaquat, Noshaba (College of Electrical & Mechanical Engineering (CEME), NUST, 2024-04)
    Human spine is a complex structure that plays a vital role in the movement, protection, and support of the body so it is very important to follow proper spine bio-mechanics to avoid any unwanted effect on body. Spinal ...
  • Classification of standard Datasets with Deep Convolutional Neural Networks using DIGITS 
    Haq, Sohrab ul (College of Electrical & Mechanical Engineering (CEME), NUST, 2017)
    Image classification is the task of finding class of an input image or probabilities of the classes that best describe an input image. Deep Convolutional Neural networks (CNN) are considered nowadays a popular deep ...
  • Fundus Image Mosaic Generation For Large Field of View 
    Ahmad, Tanveer (College of Electrical & Mechanical Engineering (CEME), NUST, 2014)
    Considering the importance of retina in the eye as many ocular and systemic diseases are manifested in the retina, the need for detailed study of 2-D retinal images, 3-D retinal images and panoramic retinal images is ...
  • fMRI based Diagnosis of Epilepsy using Functional Differences of Brain 
    BASIT, ABDUL (College of Electrical & Mechanical Engineering (CEME), NUST, 2014)
    Epilepsy is one of the major neurobiological disorders and it can induce asymmetries of brain regional activation and connectivity patterns in epileptic patients as compared to healthy controls. These asymmetries can ...
  • A Novel Approach to calculate Dominance Based Rough Sets Approximations for dynamic datasets 
    Nawaz, Uzma (College of Electrical & Mechanical Engineering (CEME), NUST, 2024-02)
    Effective data analysis of big datasets is a challenge and when data changes over time, new feature values are added, as a result, its complexity increases. An advanced approach of rough sets theory known as the ...
  • Customized RISC-V Architecture for DSP Applications 
    Masood, Marium (College of Electrical & Mechanical Engineering (CEME), NUST, 2024)
    Abstract This thesis presents a comprehensive analysis of security considerations within the RISC-V open-source instruction set architecture (ISA), highlighting its unique position due to its transparent development ...
  • Detection of Melanoma in Dermoscopy Images-Using Local Binary Patterns 
    Naeem, Sidra (College of Electrical & Mechanical Engineering (CEME), NUST, 2015)
    This thesis is motivated by the potential gains that can be achieved by the use of computer assisted decision systems (CAD) for diagnosis of melanoma in the skin using dermoscopy. A CAD system provides quantitative and ...
  • Masked Face Detection and Recognition from Images 
    Iftikhar, Aroobah (College of Electrical & Mechanical Engineering (CEME), NUST, 2023-09)
    The research field of computer vision has recently taken interest in the active problem of masked face recognition due to the COVID 19 pandemic. The use of face masks as a preventative measure against the spread of ...
  • Power Analysis Based Side Channel Attacks (SCAs) on FPGA 
    Hasnain, Ali (College of Electrical & Mechanical Engineering (CEME), NUST, 2023-09)
    In today’s interconnected world, a vast number of computer systems are globally connected, creating a global village. These systems are remotely accessible and share a significant amount of data on the cloud, raising ...

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