Department of Computer Science

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1 of 73 31 December 2021 Sales Forecasting System
(Library Information Services, CUI Lahore, 2022) Usama Ali Amin; SP18- BCS-173; .
Our project will assist to come up with better decision for business growth. Our Sales Forecasting System will be able to integrate with the existing POS system. The basic features will be already working in the existing POS, our system will assist it for different kinds of predictions. Our system will be working with less human interaction which means chances of mistake and wrong output would be negligible. In detail if we say, our system will assist the administration of the business organization for their hot selling and most favourite products which will be concluded from the customer behaviour. It means that it will assist to tell how customer buy products from you, which products is selling less, so you should less invest on these products. Our system will also provide the time-based prediction alerts. User will interact minimally with the system. He will use the system to view different predictions and alerts
3D Surface Reconstruction using Point Cloud Segmentation with Machine Learning
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Naveed Ahsan Ali Raza; FA17-RCS-018; LHR TP 5977; Dr. Zulfiqar Habib
3D point cloud is a kind of geometric data-structure, which is easily acquired using highly advanced 3D sensors. These sensing devices are capable of producing a considerable amount of 3D point clouds by capturing the 3D surface geometries and dimensions from targeted objects and real-world scenes. The captured 3D point clouds are utilized to reconstruct 3D objects, surfaces, shapes, and parts. 3D surface reconstruction is widely used in reverse engineering, 3D medical imaging, 3D printing, and automated medical surgery. Discrete surface reconstruction from 3D point clouds is still a challenging task, because captured data is un-ordered, noisy, redundant, and has topological imperfections. Due to these issues, it is not adequate for 3D surface reconstruction in its current form. In this direction, techniques based on geometric modeling have already been proposed to solve 3D surface reconstruction problems from point cloud data. Due to the irregular format of 3D point clouds, many researchers convert the data into 3D voxel grids or collection of images, which add unnecessary volume and cause issues. On the other hand, most of the deep learning-based techniques have focused on regular input data for surface reconstruction, and little attention has been paid towards 3D point cloud data. Therefore, to address the issues mentioned above, the proposed model is based on unified deep net architecture. The proposed deep net architecture takes raw 3D point clouds as an input. It performs a segmentation technique to tackle un-ordered data and further utilizes this segmentation information for 3D surface reconstruction of objects and their parts. To evaluate the performance of proposed model, experiments have been performed on the benchmark ShapeNet dataset. This dataset is a large-scale repository of 3D point clouds containing 16,880 objects of 16 different categories. The results show that the proposed method achieves superior results as compared to state-of-the art methods with an accuracy of 82% for 3D surface reconstruction
A Benchmark Corpus and Methods for the Urdu Qur’anic Question-Answering System
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Abida Ashraf; SP23-RCS-002; Dr. Rao Muhammad Adeel Nawab; LHR TP 9497
Question Answering System aims to provide answers to a question posed in natural language. A question can be factoid and non-factoid. With the rapid increase in technologies, the QA task become one of the most challenging and important tasks of Natural Language Processing (NLP). In previous studies, the problem of the QA system for the Holy Quran has been explored for Arabic, English, and Indonesian languages. Urdu has more than 170 million speakers worldwide yet considered low low-resource language. However, the problem of the QA system for the Holy Quran has not been explored for the Urdu language due to the unavailability of the dataset. To overcome this research gap, this study developed a benchmark corpus of the Holy Quran in the Urdu language. We developed the corpus by carefully mapping the QRCD corpus which was originally in Arabic language into Urdu. For the mapping of answers, we take the expertise of data annotators who have a deep knowledge of the Holy Quran in both Arabic and Urdu language. We conducted experiments in a state-of-the-art BERT based LLMs. We evaluate the proposed techniques using Exact Match, partial Reciprocal Rank, and F1 score, and expert judgment.
A Comparative Analysis of Offensive Language Detection in Roman Urdu
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Muneeb Rehman Khan; FA19-RCS-019; LHR TP 8305; Dr. Muhammad Waqas Anwar
The rise of social media has encouraged experts to conduct several studies based on user generated data. The widespread use of online platforms like Facebook, Twitter and other web forums has created challenges to keep check for inappropriate language. Pakistan has about 44 million online social network users that speak Roman Urdu to interact with each other. The prevalence of user-generated information on social media caused a massive rise in the content of offensive texts. However, this growth also offers avenues for trolls who contaminate these social platforms with their nasty remarks. In South Asia, Roman Urdu is the most widely used communication language on social media. Compared to other resource-rich languages such as English, less work has been done on offensive language classification in Roman Urdu. In this research (1) a lexicon of offensive terms in Roman Urdu is presented, (2) developed a dataset called RU-OLD “Roman Urdu Offensive Language Detection” with 15000 tweets, and (3) conducted a comparative analysis of machine learning and several deep learning architectures. Comparative analysis was performed by well-known machine learning classifiers such as Naive Bayes, RF, SVM, and Logistic Regression as well as cutting-edge deep neural networks such as Long Short-Term Memory, Convolutional Neural Networks, XLNET and BERT. XLNET trained with developed corpus outperformed the rest of the models and resulted in the highest accuracy of 93%. The results are suggestive of the scalability of the proposed approach on large scale to counter offensive language in Roman Urdu
A Context-Aware Localized Federated Learning Approach for Cloudlet Federation
(Library Information Services COMSATS University Islamaabad Lahore Campus, 2020) Sana Latif; SP18-RCS-019; Dr. Syed Asad Hussain; LHR TP 6404
Cloud computing (CC) provides storage and computation capability to perform resource intensive Machine Learning (ML) tasks for prediction and decision making. However, cloud services can introduce latency and bandwidth limitations while transmitting data to a remote cloud. Cloudlets are deployed as stand-alone devices at the network edge to bring computation in the closer proximity of users to address cloud computing challenges. Moreover, cloudlets have insufficient resources to train these resource-intensive deep learning models. Therefore, deployment of cloudlet federation can resolve latency, storage, computation, and bandwidth limitations by offloading tasks to a cloudlet within the federation. The selection of a deep learning model to reduce communication and computation cost is a challenge, as cloudlets are not context-aware in terms of network load and latency in the cloudlet federation. Moreover, deep learning model accuracy and prediction results can be affected if end-user devices are unreliable and provide incorrect data for training deep learning models at cloudlets. A cloudlet federation based novel solution is discussed in this thesis for Federated Learning (FL) that monitors network load and resources using a broker. The broker is a centralized entity that will reside within the federation and the global model will be stored inside the broker to make localized decision. COVID-19 X-ray images are used to train the model. The data is divided into 70% training and 30% validation data sets. Besides cloudlets broker will have its dataset to check the validation accuracy of the global model. The sampling data and network parameters such as available storage will be used to extract a context-aware local model for each cloudlet from the generic model. The local model will be converged using Root Mean Square Proportional (RMSProp) based on the mean squared error loss function as it provides higher training accuracy, irrespective of training data size. Convolutional Neural Network (CNN) is used in this thesis with 6 layers for classification of data, one average pooling layer, a dense and a dropout layer. The trained model on each cloudlet is sent to the broker for aggregation. Two aggregation methods are proposed in the thesis. The first one uses the layer aggregation method and the second one is based on the best model selection. Vertical Federated Learning (VFL) is used for model training. Experiments with method one gives a model accuracy of 86% and loss is 14%. However, the best model selection method generates a model ix accuracy of 96% and a loss of 15%. Total time taken for model convergence using conventional FL architecture is approximately 30 minutes. However, the designed architecture takes approximately 28 minutes for completion of global epochs. Based on the results obtained it can be concluded that the designed testbed can identify COVID-19 infected person using X-ray image or CT-Scan with 86% accuracy using FedAvg algorithm and with 96% accuracy using best model selection.
A Context-Aware Localized Federated Learning Approach for Cloudlet Federation
(LHR TP 6404, 2021) Sana Latif; SP18-RCS-019; Dr. Syed Asad Hussain; LHR TP 6404
Cloud computing (CC) provides storage and computation capability to perform resource intensive Machine Learning (ML) tasks for prediction and decision making. However, cloud services can introduce latency and bandwidth limitations while transmitting data to a remote cloud. Cloudlets are deployed as stand-alone devices at the network edge to bring computation in the closer proximity of users to address cloud computing challenges. Moreover, cloudlets have insufficient resources to train these resource-intensive deep learning models. Therefore, deployment of cloudlet federation can resolve latency, storage, computation, and bandwidth limitations by offloading tasks to a cloudlet within the federation. The selection of a deep learning model to reduce communication and computation cost is a challenge, as cloudlets are not context-aware in terms of network load and latency in the cloudlet federation. Moreover, deep learning model accuracy and prediction results can be affected if end-user devices are unreliable and provide incorrect data for training deep learning models at cloudlets. A cloudlet federation based novel solution is discussed in this thesis for Federated Learning (FL) that monitors network load and resources using a broker. The broker is a centralized entity that will reside within the federation and the global model will be stored inside the broker to make localized decision. COVID-19 X-ray images are used to train the model. The data is divided into 70% training and 30% validation data sets. Besides cloudlets broker will have its dataset to check the validation accuracy of the global model. The sampling data and network parameters such as available storage will be used to extract a context-aware local model for each cloudlet from the generic model. The local model will be converged using Root Mean Square Proportional (RMSProp) based on the mean squared error loss function as it provides higher training accuracy, irrespective of training data size. Convolutional Neural Network (CNN) is used in this thesis with 6 layers for classification of data, one average pooling layer, a dense and a dropout layer. The trained model on each cloudlet is sent to the broker for aggregation. Two aggregation methods are proposed in the thesis. The first one uses the layer aggregation method and the second one is based on the best model selection. Vertical Federated Learning (VFL) is used for model training. Experiments with method one gives a model accuracy of 86% and loss is 14%. However, the best model selection method generates a model ix accuracy of 96% and a loss of 15%. Total time taken for model convergence using conventional FL architecture is approximately 30 minutes. However, the designed architecture takes approximately 28 minutes for completion of global epochs. Based on the results obtained it can be concluded that the designed testbed can identify COVID-19 infected person using X-ray image or CT-Scan with 86% accuracy using FedAvg algorithm and with 96% accuracy using best model selection.
A Deep Learning Based Prediction of Stock Market Trend using Social Medi
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Aroma Javed; FA20-RCS-006/; LHR TP 8339; Dr. Hamid Turab Mirza
Machine learning and deep learning are becoming more and more effective techniques for evaluating financial data, encompassing textual, statistical, and digital information. Future stock prediction is a prominent and challenging deep learning topic in the industry. The difficulty in predicting future stock market stems from too many diverse elements that simultaneously influence the amplitude and frequency of stock market rise and falls. In this research work, the main focus is on the problem of stock market trends predictions using social media as a tool. Digital networks are a fast-growing area of information on the Internet. Perhaps one of the most important features is the instant availability of more knowledge and the users' ability to converse swiftly. Different Deep Learning algorithms (like CNN, RNN, GRU, and Bi Directional RNN) were used to forecast stock market trends based on information from social media, as this data might influence investor behavior. Algorithms were used to investigate the impact of social media accounts on stock market prediction performance. The dataset chosen was an expert and public Twitter post from two prominent technology firms, Alphabet Inc. (Google) and Apple Inc, and news data related to these famous firms. The thesis employed deep learning methods, a pre trained language model for economic sentiment analysis, to extract sentiments from tweets. With the help of this research, it will become easy for an investor to invest his money in companies whose stock market values are high on the basis of sentiment classification and will not lead them to any financial crises. SMP aims to anticipate how the stock value of an economic trade will fluctuate in the foreseeable. If shareholders can precisely estimate stock market progression, investors will indeed be able to turn a profit. Finally, the study predicted the trends by modeling the Data on the proposed GRU model, which outperforms the result of other algorithms. The GRU model has shown significant results with an accuracy of 82.41%.
A Deep Learning Model for Efficient Effort Estimation in Scrum Projects
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Ahmad Amjad; FA18-RCS-028; LHR TP 8041; Dr. Farrukh Zeshan
Software Development Effort Estimation (SDEE) is defined as the process of predicting the effort required to develop a software system. Effort estimation at the earlier stage in scrum project is specially a challenging task because the requirements are volatile in nature and it can be changeable at any phase of the whole project life cycle. A project manager needs estimation for making a decision and predicting the total budget. The success or failure of software development depends on the accurate estimation of efforts required to complete a project. In literature, the three basic ways are explored for efforts estimation in scrum projects which are purely model based method, the expert-based methods and hybrid-based methods. In the model-based method the data collected from similar previous project to make prediction about the new project, the expert-based method totally dependent on human expertise to make such kind of judgement whereas the hybrid method are the combination of both model based and expert based methods. Every method has different challenges but in the era of agile software development methodologies, traditional planning and software effort estimation methods are replaced to meet customer’s satisfaction in agile environments. However, software effort estimation remains a challenge. Although teams have achieved better accuracy in estimating story points effort required to implement user stories or issues, these estimations mostly rely on subjective assessments, leading to inaccuracy and impacting software project delivery. Some researchers are pointing good results by the adoption of deep learning to address this issue. Given the foregoing, this study proposes a deep learning model for story points estimationin t h e scrum projects. In this study proposed a model which used the combination of Bidirectional LSTM and Recurrent Highway Network two powerful deep learning techniques using the story points for efforts estimation in the scrum projects. The proposed model is trained over a large d a t a s e t s for story points estimation made by 16 open-source projects which have 23313 issue that are large story point datasets according to my knowledge. The performance of the proposed model has validated through the evaluation techniques such as Mean absolute error (MAE), Mean square error (MSE), and Median absolute error (MdAE). The results shown that the proposed model provides betters result as compared to previous approaches
A Digital Twin Assisted Disease Detection for Leafy Green Vegetables Using Federated Learning in Smart Greenhouse
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Iqra Pervez; CIIT/SP23-RCS-016/LHR; Dr. Tariq Umer; LHR TP 9703
Agricultural production is seriously vulnerable to the rapid evolution of plant diseases in greenhouse environment. In modern agriculture, ensuring timely and accurate disease detection in leafy green vegetables is crucial for improving crop yield and sustainability. The effects of environmental temperature, humidity on plant’s health which restricts the efficacy of current disease management techniques in greenhouses. This research presents a Digital Twin-assisted model for disease detection in lettuce plants, leveraging Federated-based Learning to enable privacy-preserving and distributed model training within a smart greenhouse environment. The study integrates five pre-trained models AlexNet, MobileNet, ResNet50, EfficientNetB3, and VGG16 to evaluate their effectiveness in classifying plant diseases. This study utilise a lettuce diseases dataset from Kaggle. Among the tested models, VGG16 achieved the highest accuracy of 98%, demonstrating its superior capability for disease classification in resource-constrained settings. Additionally, a correlation analysis between humidity, temperature and disease occurrence was conducted using Unity 3D and Microsoft Azure, providing deeper insights into the environmental conditions influencing plant health. The proposed model is useful for monitoring crops in real time and shows that combining Digital Twin technology with Federated-based Learning can improve decision-making in smart farming systems.
A Digital Twin based Approach for Healthier Smart City Environment
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Khazina Naveed; FA21-RCS-001; Dr. Tariq Umer
Air quality, toxic substances, climate change, pollution, and occupational hazards are the environmental factors that have a great impact on human and environmental health. The presence of pollutants and contaminants in the environment can have detrimental effects on human health, including respiratory issues, cardiovascular ailments, cancer, neurological disorders, and various other illnesses. Certain groups, such as children, older adults, and individuals with pre-existing health conditions, are more vulnerable to the health impacts of environmental hazards. Air Quality Index is used to depict the air quality of an area. This thesis delves into studying the utilization of Digital Twin models as an innovative strategy for creating a sustainable smart city environment by accurately forecasting Air Quality Index (AQI) using time series analysis. The study applies different deep learning models, namely Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and deep Artificial neural networks (ANN) for precisely predicting the AQI levels for a healthier environment. The time-series historical data of Delhi city has been gathered from the year 2015 to 2020 and is preprocessed before proceeding to train and validate different deep learning models. The six selected machine learning algorithms have been implemented and it has been observed that CNN offers unparalleled accuracy compared to other evaluated models making it highly effective for precise forecasting. The CNN-1D-2 layer yielded the best results with root mean squared error (RMSE) reaching 3.010343, the mean absolute error (MAE) reaching 1.706329, the mean absolute percentage error (MAPE) reaching 0.013216, and the R2 reaching 0.99931. The digital twin model is developed by incorporating InfluxDB and Grafana. InfluxDB is an open-source platform that has been used to store the historical and real-time data of AQI using python. The Grafana online platform is utilized for data visualization and management and facilitates real-time monitoring. The 3D model of the city is developed in Blender and then the 3D file is exported to Microsoft Azure Digital Twin explorer to develop a digital twin using Digital Twin Definition Language (DTDL). The digital twin model displays the forecasted value of AQI and different pollutants along with their previous trends in the form of graphs. The 3D model of the city is covered with different colors based on ranges or thresholds defined for AQI. The findings contribute to the advancement of smart city development by utilizing digital twin-based approaches for creating healthier and more sustainable urban environments
A Digital Twin-Assisted Hybrid Decision-Making Model for Autonomous Vehicles Behavior Analysis
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Hina Saleem; SP22-RCS-005; LHR TP 8682; Dr. Tariq Umer
This thesis aims to center its attention on the development of a prototype of a Digital Twin that can be utilized for the analysis of autonomous vehicle behavior. The objective of the project was to explore the potential connection and configuration of Digital Twin and Autonomous Vehicles in order to create a visualization-based model for analyzing vehicle behavior. Two methods were employed to establish the connection between autonomous vehicles and a Digital Twin. The first method involved simulating a instinctive model using historical data to recreate scenarios. The second method involved simulating a computational model to incorporate a texture from the physical environment into the DT. In order to initiate the configuration of the DT, Carla was employed to generate states representing the ahead going speed and acceleration of a vehicle, thereby allowing for testing of the OpenModelica models. Additionally, efforts were made to increase the statistics by utilizing Carla as a realistic tool to integrate Autonomous Vehicles with Carla. This thesis introduces a configuration that introduces the concept of the DT being one step ahead of the PT. The PT, which is based on machine learning, was introduced as a means of generating similar parameters to those of a vehicle in a physical environment, thereby allowing for testing of this setting. However, the research did not inspect methods for integrating the same 3D geography in Carla and the machine learning-based simulator, which limited the experiments conducted while the machine learning simulators served as the PT. Furthermore, the statistics between Carla and OM was unable to investigate the concept of reflecting x back action to the PT. Consequently, a second design diagram, based on the knowledge acquired in this project, is presented in the thesis, with the potential for further investigation. In conclusion, this thesis presents a Digital Twin-based methodologies utilized throughout the project establish a strong foundation for future research in the development of a Digital Twin-based model for autonomous vehicles
A Framework for the Development of Cybersecurity Legislation using a Decision Support System
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Naseer Ahmad; FA21-RCS-017; LHR TP 8679; Dr. Adnan Ahmad
The dependence on technology is increasing and its influence is felt in practically every aspect of life. Its trend is expected to remain in the future. Unfortunately, its advancement has led to an increase in cybercrime and it is causing growing concern around the world, specifically for developing nations. Developed countries have implemented strong cybersecurity legislation to address this issue. It is imperative that all nations implement robust and uniform cybersecurity legislation to combat cybercrime, similar to that of advanced nations. For this purpose, A framework is essential to assist in creating an effective and robust cybersecurity legislation, which is necessary to combat the negative impacts of cybercrimes. For fulfilling this need, this research has proposed a framework for the development of cyber law using a knowledge-based decision support system.
A Game Theory Based Trust Model for Internet of Things
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Ghulam Mustafa; FA15-RCS-029; LHR TP 5970; Dr. Adnan Ahmed
Internet of Things is a paradigm shift in the advancement of wireless sensor networks. This has been realized with advanced techniques of service provision and exchange of data. The concept behind this technology is “anytime, anywhere, any media”. There is a huge potential in IoT for the development of a large number of application out of which very small part has been deployed in our lives. Devices connected through the internet of Things are equipped with smart objects and these objects are increasingly becoming part of our life. IoT devices are providing services in almost all walks of life like services in automated industry, medical field, chemical plants, inventory control and other areas of human life. IoT devices are light weight with low power and low memory. Therefore, these devices are resource constrained and data possessed by these devices is very sensitive. In this research work the author identified a number of trust factors for the differentiation of the trustworthy and suspicious devices in IoT environments. The suspicious devices some time behave abnormally due to number of facts like memory and battery constraints and low processing power. Due to their malfunctioning these devices compromise the overall trust profile of the IoT network. The author has proposed a framework through which he described the essential elements for the identification of malicious nodes. This framework helps in in identifying the suspicious behavior of giving false feedback as badmouthing against the trustworthy nodes. After framing the proposed frame work, a model has been designed in which game theory has been incorporated for the behavior correction of the malicious devices through carrot and stick policy. On the basis of the game strategy the Nash equilibrium has been achieved so that a device after choosing its strategy cannot deviate from its strategy unilaterally. The stated model has been formalized in Microsoft Azure IoT Central, a software as a service (SaaS) based cloud environment and simulation has been performed. In the simulation, the deployment of the IoT devices and their feedback values regarding the tasks and services have also been collected for the identification of badmouthing in these feedbacks. The trust value of the participating nodes has been calculated on the basis of the feedback values after passing through the proposed non cooperative game so that our proposed trust model become trustworthy and cooperation enforcing. Finally, a conclusion regarding the performance of proposed work has been drawn
A Lightweight Indoor Smoke Detector with Benchmark Dataset using Deep Learning
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Raheel Shahzad; FA19-RCS-007; LHR TP 7602; Dr. Allah Bux Sargano
A fire disaster is one of the most dangerous events that may occur at any place and time. To avoid such incidents, smoke detection is crucial before getting fire. The smoke sensors are the most widely used devices to detect fires; however, smoke sensors can only detect fires if the fire is large, and smoke reaches the sensor. Therefore, vision based smoke detectors have been proposed using machine learning and deep learning based methods. Most of these methods have been proposed for outdoor smoke detection, while little attention has been paid to indoor smoke detection due to the lack of appropriate datasets for indoor scenarios. This study creates a benchmark dataset for indoor smoke detection by properly following annotation criteria outlined by Inter Annotator Agreement (IAA) and Cohen’s Kappa evaluation metrics. The proposed dataset achieved 0.91 IAA and 0.81 Cohen’s Kappa scores, which confirms the excellent quality of the dataset. In addition to this, an innovative transfer learning-based method has been proposed for indoor smoke detection and evaluated on the proposed dataset. Furthermore, a state-of-the-art smoke detection algorithm has been implemented and evaluated on the proposed dataset for comparative analysis. The results confirm that the proposed method outperforms the state-of-the-art methods.
A Machine Learning Approach for Mutation Identification in Gene Sequences to Detect Lungs Cancer
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Umer Farooq; SP19-RCS-029; LHR TP 8334; Dr. Allah Bux Sargano
Cancer is one of the leading causes of death worldwide. The mutation is one of the basic causes of cancer, which is caused by any change in the gene sequences of the human body. The abnormal and uncontrolled growth in the cells of the lungs is a cause of lung cancer. It is the second most common type of cancer worldwide, considered 11.2% of all cancers. According to the cancer statistics of the United States of America (USA), about 236,740 people were diagnosed with lung cancer in the year 2021, and about 130,180 people died from lung cancer in the same year. This study aims to develop a dataset, machine learning, and deep learning-based techniques to address the above-mentioned issues. In this regard, the asia.ensembl.org website is used to extract normal gene sequences from the lung cancer dataset, while the mutated gene sequences for lung cancer are obtained from the intogen.org website. Thus, the dataset of 1588 samples consisting of 2576 mutations is developed by following the standard dataset creation method. Then, different feature extraction methods are applied to extract the discriminative features from the dataset; these features include raw moment, Hahn moment, central moment, position relative incident matrix (PRIM), reverse position relative incident matrix (RPRIM) calculation, feature vector determination, accumulative absolute position incidence vector (AAPIV), reverse accumulative absolute position incidence vector (RAAPIV). These features are then fed to the traditional machine learning (ML) algorithms such as Random Forest (RF), Logistic Regression (LR), Multi-Layer Perceptron (MLP), and an ensemble of these traditional ML algorithms. These extracted features are also fed to deep learning (DL) methods such as LSTM, GRU, Bi-LSTM, and an ensemble of these DL algorithms. An averaging technique is applied to ensemble the classification algorithms. Three validation methods are used for evaluation: self consistency testing, 10-fold cross-validation, and independent set testing. The final classification result of lung cancer is generated as accuracy, specificity, Mathew's Correlation Coefficient (MCC), sensitivity, and Cohen's Kappa. The efficacy of this study can be measured with the accuracy of 94.5% obtained from the ensemble of x machine learning models using 10-FCV and with the accuracy of 97.3% obtained from the ensemble of deep learning models using the independent set test.
A Machine Learning Technique for Motion Planning in Articulated Robots
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Moman Ali Haider; SP20-RCS-024; LHR TP 8670; Dr. Wajahat Mahmood Qazi
The integration of human-like motion into robotic systems has emerged as a pivotal research frontier, driven by the aspiration to create robots capable of nuanced interactions in real-world scenarios. This thesis delves into the development and implementation of a novel solution, leveraging state-of-the-art motion planning algorithms and neural network based technologies to instill human-like qualities in robotic motion. The research unfolds against the backdrop of contemporary challenges in the field of robotics, particularly the need for motion planning algorithms (MPAs) that not only navigate collision-free paths but also emulate human-like movements. In the landscape of motion planning, traditional algorithms like rapidly exploring random trees (RRT) and its variants have proven effective, yet their computational complexity becomes a bottleneck in higher-dimensional problem spaces. To address this limitation, the motion planning networks (MPNet) paradigm is introduced, utilizing a neural network approach with point cloud representations to navigate the intricacies of higher-dimensional environments. The computational efficiency of MPNet is harnessed to overcome challenges associated with dimensionality, offering a promising avenue for generating collision-free paths. The central problem addressed by this research is the lack of human-likeness in the paths generated by MPAs, limiting their applicability in tasks that demand human-like motion. Prior attempts to imbue human-likeness often relied on datasets recorded from human movements, leading to unreliable and constrained solutions. In response, the proposed solution adopts a hybrid approach, combining the strengths of MPNet and Artificial x VFRRT. The latter is chosen for its ability to generate human-like paths, albeit with limitations in higher-dimensional problems. The research methodology unfolds in distinct phases, beginning with the development of a reliable human-like motion dataset. The Data Generator (DG) module orchestrates this process, employing an Artificial VFRRT-based motion planner within the Kautham simulation tool. The dataset, characterized by dynamic path generation strategies and environmental diversity enhancements, forms the foundational building block for subsequent modules. The Data Encoder (DE) module steps in to transform raw obstacle representations into a point cloud format compatible with MPNet training. This adaptive encoding ensures usability and efficiency, setting the stage for the Human-like MPNet (HLMPNet) module. HLMPNet marks a paradigm shift in motion planning architectures, dynamically adapting learning parameters through an iterative process informed by human-likeness evaluations. This module is designed not only to replicate human motions but to refine and adapt its behavior based on nuanced feedback. The Human-Likeness Evaluator module acts as the discerning judge in the evaluation framework, quantifying the authenticity of generated paths. Its role in continuous learning and optimization ensures that HLMPNet evolves towards increasingly authentic and nuanced human-like motion planning. The significance of this research lies in its practical applications across various domains, including real-world human-robot collaboration, user-friendly interfaces, efficient and safe robotic operations, enhanced experiences in entertainment and services, and improved assistive and rehabilitation technologies. The proposed solution offers a holistic approach to addressing the challenges of human-like motion in robotics, contributing to the ongoing evolution of robotic systems in diverse applications
A Methodological Approach for Classifying and Differentiating Business Processes Using Tasks Label
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Sidra Anwar; SP23-RCS-027; Dr. Abid Sohail; LHR TP 9492
It is crucial for two organizations to integrate their business process models when one acquires the other or merges with another organization. This research is motivated by the problem of matching processes with semantically similar activity names and similar process maps that make alignment difficult. Introducing a new approach based on the Domain Specification Mapper along with the current state-of-art transformer models, BERT for semantic augmentation and contextual interpretation of activity labels. The method combines semantic analysis, syntactic features and machine learning algorithms to show that DSM does not only increase the effectiveness of the transformer models such as BERT, but also increases the effectiveness of other machine learning models. Although traditional classifiers and semantic syntactic features failed to provide a correct classification of processes, DSM with BERT can successfully address these problems. Furthermore, the integration of DSM with other classifiers also showed good results and improved the classification rate. This approach facilitates integration of process, increases accuracy and aligns operations which makes it ideal for mergers and acquisitions.
A Methodological Exploration of BPM Community
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2017) Saman R. Abdi; FA15-RCS-007; LHR TP 6831; Dr. Abid Sohail
The study is motivated to explore the collaboration between the authors, the patterns which are being used by the authors and the areas on which the authors are working. The end product of the research is a set of generic guidelines for the field of business process management. Business process management conference, which was one of the most famous conference is been used to develop the dataset for the study. Business process management conference started since 2003 so the papers since 2003 till 2016 were collected to create the dataset. The collaboration of authors is been studied by applying certain filters over the raw data of business process management conference. The research pattern of different authors was been studied individually from each paper and later they were all combined by identifying the common or similar aspects of the processes to form a set of frameworks. The study has created a generic set of five frameworks. Each for a specific domain of business process management i.e. business process similarity, business process abstraction, business process model redesign, business process monitoring, and business process mining. The study also provides some key facts about the most collaborative authors and the domains on which the authors or the research community is mainly focusing.
A Performance Centric Cloudlet Federation Paradigm
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) Muhammad Zaid Nayyer; FA14-PcS-001; LHR TP 5915; Syed Asad Hussain
A Person Reidentification Framework for Multi- Camera Tracking by Using Tracking-by-Detection Paradigm
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Mian Muhammad Abu Bakar; CIIT/FA22-RCS-008/LHR; Prof. Dr. Zulfiqar Habib; LHR TP 9697
The research is based on offline video-based reidentification of individuals by tracking them across multiple non-overlapping cameras, aiming to achieve accurate, efficient, and effective video-based person reidentification by extracting discriminative human attributes and robustly utilizing spatio-temporal complementary features in the tracking process. Person reidentification (Re-ID) aims to identify individuals with the same identities across multiple cameras at particular or different instances of time from images or video sequences. It involves applying deep learning algorithms that help to track people’s positions, motion, and direction. Over the past few years, deep learning- based methods have achieved significant success in this domain. These methods can be categorized as deep metric learning, micro-level feature learning, generative adversarial learning, domain adaptation and transfer learning, sequence feature learning, transformer-based methods, and clustering-based methods. Specifically, sequence feature learning, which focuses on fusion of spatio-temporal information from multiple dimensions, and transfer-learning-based methods, which focus on using pre-trained models trained in one domain and fine-tuning these models by providing samples of the target domain. These methods have achieved better experimental performances compared to the other deep learning-based methods, with significant improvements in accuracy. In the context of person reidentification, persistent challenges such as heavy occlusion and similar appearances, which lead to ID switches, that affect the person reidentification and tracking process, still remain an active area of research. To address these issues, a Single-Camera People Tracking mechanism named Redundancy- Elimination Clustering (REC) is introduced. ID switches mostly occur due to background clutter, heavy occlusion, similar appearances of individuals, or when two or more individuals cross each other in a camera view. These ID switches affect the process of reidentification and tracking. As a person cannot be tracked in different locations at the same time, it becomes clear that the possibility of the occurrence of the ID switches is due to the presence of multiple individuals in the same frame. The frames in which there are multiple individuals are termed here as the Redundant Frames, and we employ Redundancy-Elimination Clustering (REC) to resolve this issue through Hierarchical Clustering. To handle ID switches across multiple cameras, we propose Feature Supervised Clustering (FSC), which utilizes the Hungarian Algorithm and assigns unique global IDs to individuals, enabling robust cross-camera association. To correct the incorrect global IDs assigned to the individuals in the previous Feature Supervised Clustering (FSC) stage, either due to very similar appearances or due to extreme occlusion, we introduce Cross-Camera ID Refinement (CCIR). This method effectively assigns unique global IDs to individuals. In the end, some post-processing, like linear interpolation and handling of edge cases, is also performed. The final proposed method achieved IDF1 (95.67), IDP (96.26), IDR (95.13), Precision (96.26) and Recall (95.86), compared to the base methodology which achieved IDF1 (93.72), IDP (91.8), IDR (95.75), Precision (91.83) and Recall (95.64), which shows the robustness of the proposed method.