Browsing by Author "Dr. Abid Sohail Bhutta"

Now showing 1 - 6 of 6

AI for Media Fairness: Detecting Bias in English News
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Fozia Mujahid; CIIT/SP24-RCS-002/LHR; Dr. Abid Sohail Bhutta; LHR TP 10035
The digital journalism growth has enabled many more people to have access to a larger amount of information and news; however, this increased access to the internet and news has also brought attention to an increasing number of additional concerns regarding the influence of bias on how news media report, produce and share their content [1]. In this thesis, we explore and research multiple forms of bias found within English language based media and offer an AI-powered solution to identifying and classifying bias within the English language media [2]. The focus of our research is the major forms of media bias – i.e., (i) language bias; (ii) selection bias; (iii) framing bias; (iv) sentiment bias; and (v) ideological bias. The main data source used in this research is a publically available data corpus known as the Media Bias Identification Corpus (MBIC) which consists of labeled media articles written in the English language. We then utilize Natural Language Processing (NLP) techniques to preprocess the text and to obtain features, and subsequently apply several different types of machine learning and deep learning models to classify media biased content [3]. We thoroughly evaluate the performance of all of the trained models using standard performance metrics in order to compare their classification performance, as well as to apply topic modeling to evaluate how media topics are associated with particular forms of bias. Our evaluation of the experimental results found that framing bias and sentiment-based forms of bias were the two most prevalent forms of bias found within media articles written in English. Finally, the machine learning, and more specifically the deep learning and transformer based, models outperformed models for the purpose of detecting media bias in English written articles. The results of our research will aid in developing a fair and transparent approach to digital journalism for media organizations, by providing insight into how the ty
Automatic Transformation of BPMN Models Onto Petri Nets
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Samia Nazeer; SP21-RCS-003; LHR TP 8672; Dr. Abid Sohail Bhutta
Software success depends on the accuracy of the business process modelling notation (BPMN), and the correctness verification procedure is built upon the formalization of BPMN. The primary drawbacks of the BPMN formalization under shifting business needs are, however, the inability to dynamically adapt the formalized BPMN model to modifications in the BPMN model. For the purpose of modelling business processes, the Business Process Model and Notation standard is frequently utilized. Its primary flaw, though, is that it lacks formal semantics, which results in several unwanted qualities like deadlocks and livelocks which produces models with semantic flaws. Petri nets are a formal tool based on sound mathematical ideas and can handle analysis. Additionally, it provides several analytical methods. To officially validate them, we must convert it into a formal language, like Petri nets. The majority of earlier studies focused on manual transformations by mapping rules which creates static petri net model. In order to perform conformity verification, the BPMN model must be transformed into a formal process modelling representation, such as Petri nets. In order to get over these restrictions, this study suggests a method for developing a Petri net model automatically in accordance with BPMN modification rules. Firstly, this study automatically converts the Business Process Model into Petri net Model and produces dynamic output. Secondly, the converted model is compared with the manually transformed model created by expert users according to verified mapping figure’s rules and then analyze the result. Thus, the proposed approach obtains a reliable and well organized Petri net model that works well with other Petri net analysis methods as well as with human analysts
Business Processes Models Semiology Mapping using Domain Ontologies and Dictionaries
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Laraib Mumtaz; FA18-RCS-001; LHR TP 7283; Dr. Abid Sohail Bhutta
Semiotic mapping is the part of semantic quality of business processes. Semantic quality is crucial for business processes validation. It related to correctness and completeness of the business processes in problem domain. Semantic quality is concerned with semantic similarity measurement between two words and concepts. Semantic similarity is current area which is expanded widely, it attempts to calculate relatedness between words, ideas, sentences and documents. Comparability among two words is a proportion of the likeliness of their meanings, computed depends on the ideas and their relationships in ontology and taxonomy. In our research we applying some similarity measures on activity labels of model for improve the Model quality, Model matching, Model validation and it can help in generation of process models. We applying the WordNet based similarity measures between Activity labels of process models and extracted Activity labels from textual description of process models. In this research we conduct the semiology mapping of syntactic/string based and semantic based. In syntactic similarity we conduct the term based similarity in which we apply the Cosine similarity and in semantic similarity we conduct the knowledge based similarity that consists of relatedness and similarity measures. We only applying the similarity measures that consists of path length. Path length consists of WUP and path. We proposed to use cosine WUP and path similarity measures on labels for semiology mapping.
On Enhancing the Quality of Business Process Model’s Activities Labels
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Urooj Fatima; FA18-RCS-006; LHR TP 7291; Dr. Abid Sohail Bhutta
Business process models describe their operations, events, and control flows through graphical illustrations to increase the knowledge and awareness of business processes. Large corporations use models to document and design business processes. With the growing number of business process models and trained modelers, modeling initiatives demand quality assurance. Nevertheless, checking the quality of the process model, especially its activity labels, is a challenge. In labels, synonymy, vagueness, homonymy, incorrect labeling, as well as different modeling styles result in ambiguity, uncertainty, and misunderstandings. Quality of activity labels rely on precise and fitful words which are according to the domain process models taking quality parameters under consideration. The problem arises when the activity labels are too short and provide limited information and words facing the zero-derivation problem. For this purpose, algorithms have been deployed which will recognize, identify and check the labeling styles of a process model. Activity labels has been extracted automatically. Further, NLP techniques like WordNet has been used for the analysis of activity labels. In this study, the quality of textual labels in activities of a process models is addressed. Activity labels has been analyzed using a collection of business process models based on medical chronic diseases. Results obtained by deployment of algorithms on automatically extracted labels confirms the applicability and accuracy of proposed techniques.
Process Model Generation from Textual Descriptions
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Aleena Nazir; FA18-RCS-014; LHR TP 7290; Dr. Abid Sohail Bhutta
Process models are an important source to gather the information on organizational workflows and also represent the first point of process analysis and improvement. In organizations, business process modeling is an important tool to understand and automate the business processes. However, in many organizations the existing documentation of business processes is difficult to understand by the analyst. So, in real-life organizations, the high complexity of business processes is constantly raising an issue. Due to this, the establishment of process models for the business process is becoming a challenge for the stakeholders. So, the process model extraction from the business processes may helpful to minimize the process modeling effort. But, manual generation of business process models is a time taking task for the stakeholders. Yet, to assist this task, new methods can be implemented for the automation of process design phase. An approach to generate process models from the textual descriptions has been developed. In this approach, we use different natural language processing techniques to define a set of mapping rules. Through these mapping rules, we extract the elements business process models from textual descriptions and also checked the correct sequence of extracted elements for business process models. By combining the business process elements according to their sequence, process model is generated
Security Enhancement Analysis of NTDC
(LHR TP 9333, 2024) Yasir Ali; FA20-RCS-005; Dr. Abid Sohail Bhutta; LHR TP 9333
NTDC in Pakistan serves as the backbone of the country's electricity sector, with its primary purpose being the efficient and reliable transmission of electricity. NTDC not only ensures the seamless flow of electricity across long distances but also undertakes grid expansion and upgrades to accommodate growing electricity demand and integrate renewable energy sources. During the location stage, NTDC should persistently screen its organizations and frameworks for strange exercises, interruption endeavors, or indications of give and take. This incorporates constant danger location, log examination, and security occurrence checking. Early location is fundamental to answering arising dangers and limiting likely harm instantly. The purpose of current research is to implement the blockchain and get analyze the number of attacks on the under-discussed application, and analyze how secure the system is after the implementation of the Blockchain. In current research work we will analyze secure communication between back offices of the NTDC, and also analyze the number of attacks on the backend system. After complete review of existing system, blockchain base solution will be implemented for secure communication and data storage. Transactions will be stored on a distributed blockchain platform. This will provide a new digital identity built on blockchain for security and better performance. This concept ought to alleviate issues with application security in the government sector and lessen the likelihood of sensitive data being lost. After effective organization of the thought, examination will be recorded on client audits and check the quantity of assaults which will find success on blockchain. To evaluate the impact of implemented model on end user of NTDC a survey is conducted which conclude that the implemented model is way better than the older one on base of security, Assessability, sustainability, training, and perceived impact.