http://hdl.handle.net/123456789/17506 Tue, 28 Apr 2026 09:41:18 GMT 2026-04-28T09:41:18Z http://hdl.handle.net/123456789/20011 HUMAN CENTERED DESIGN AND EVALUATION OF AI APPLICATIONS IN MEDICAL HEALTH 03-243222-001, AMNA FAISAL Through this research study, the study aims to facilitate AI-based medical health applications both local and international, more accessible and useable to semi-literate users of Pakistan. At the initial stage, systematic literature review (SLR) identified critical factors regarding AI-based medical health applications and semiliterate users. Twelve factors are identified within three categories: Usability, User Experience, and Content & Design. These factors included Ease of Navigation, Memorability, Learnability, Efficiency, Effectiveness, Accessibility, User Satisfaction, Interactivity, Readability, Ease of Use, Content, and Design. MCDM techniques like AHP and Fuzzy AHP are applied to prioritize the factors. From the results, the top six significant factors are selected for further analysis; these are Ease of Navigation, Learnability, Effectiveness, User Satisfaction, Memorability, and Readability. Then, a further SLR is conducted for choosing applications, which include ratings from the application, downloaded statistics, presence of AI features, and user reviews. Next, two validation methods are applied for this research. First, we will consider the match of 10 selected AI-based health applications to prioritized factors with expert assistance using the Google Form survey. Applications in compliance with these factors are further selected for the next method: heuristic evaluation. With this heuristic evaluation, experts draw attention to several usability issues and provide recommendations to enhance the applications' usability. Among the selected three applications that are WebMD, Healthwire, and Dawaai, least user experiences were satisfied through these applications, and for this reason, they are selected in the next phase of heuristic evaluation. A prototype is created with the feedback, which included all the changes it planned to make based on the issues. The same experts reviewed this prototype to check whether the new design met their expectations and whether they fully agreed with the proposed improvements. This iterative process highlights a need for AI-based health applications to be culturally sensitive and accessible, especially for semi-literate users in Pakistan. The research focuses on critical usability factors and iterates the design based on expert feedback. The improvement achieved is in terms of usability, user experience, and accessibility of AI-based health applications to cater to diverse populations more effectively. Dr. Abdul Hafeez Sat, 01 Nov 2025 00:00:00 GMT http://hdl.handle.net/123456789/20011 2025-11-01T00:00:00Z http://hdl.handle.net/123456789/20015 TRUSTED SIMPLE NETWORK MANAGEMENT PROTOCOL FOR INTEGRITY EVALUATION IN DISTRIBUTED ENVIRONMENT 03-243222-05, Hafiz Muhammad Ashja Khan As computer technology continues to advance, the imperative for enhanced security and privacy for users becomes increasingly pronounced. A multitude of individuals are interconnected with networks in various ways, be it through mobile phones, online banking, ATM transactions, email communication, social networking, and more. With technology's pervasive integration into our daily lives, users face mounting security risks, particularly concerning the confidentiality, integrity, and accessibility of their data. Despite the myriad solutions proposed by researchers to shield user information from unauthorized access and ensure their security, grappling with these security challenges remains an arduous endeavor. In an era of rapid technological evolution, the Trusted Computing Group (TCG) has taken the lead in developing security specifications aligned with international standards for various industries. Among its notable contributions is the creation of the Trusted Platform Module (TPM), a security chip that incorporates cryptographic techniques within hardware to instill trust in computing systems. TPM functions as a specialized cryptographic processor meticulously designed to bolster system security. At its core, TPM serves the pivotal role of guaranteeing the integrity of a system. The foundation of trust is established from the moment of power-on boot, often referred to as the "core of assurance." Security-relevant gauges are securely stored in Platform Configuration Registers (PCRs), diligently monitoring, reporting any deviations from previous configurations. These reports inform decisions on how to proceed, ensuring that both users and applications running on the system can have unwavering confidence in its security. While we say the TPM checks the integrity of the individual node and their no such way to check the integrity of whole system and we have proposed that the solution is designed to centrally report the rectitude status of every system in the network. It involves extending the Simple Network Management Protocol (SNMP) to get integrity information from all network nodes and clients, along with other relevant status data and report to the administrator. A specific Object ID within the SNMP Management Information Base (MIB) has been designated to link the necessary rectitude information acquired through TPM PCR_QUOTE. This information is subsequently transmitted to the administrator in response to an SNMP GET. On the server side, the received PCR_QUOTE data is compared to the stored values representing both beneficial and harmful conditions. The proposed architecture the Simple Network Management Protocol securely gathering the integrity data of every node. Since SNMP is an open-source protocol, in this study we expand its functionality by reserving an Object Identifier (OID) to associate the extracted hash of integrity measurement. The study shows that it does not compromise the performance or integrity of the system or SNMP. Overall the proposed extended SNMP model offers a robust solution for trusted and secure network management with minimal performance impact. Fri, 01 Nov 2024 00:00:00 GMT http://hdl.handle.net/123456789/20015 2024-11-01T00:00:00Z http://hdl.handle.net/123456789/21042 ALIGNMENT OF PPI NETWORK USING NOVEL TOPOLOGICAL MEASURES 03-243222-003 ASIYA JAHANGIR Experimental Protein-Protein Interaction (PPI) Network data has been collected using high-throughput PPI profiling techniques. PPI network analysis aids in the molecular-level understanding of the proteins. The PPI network alignment can reveal the relationships between multiple species, which improves our knowledge of biological systems and helps to transfer knowledge across the species. PPI network alignment's primary goal is to build a combined network that helps research intricate pathways to identify the roles of unidentified proteins. It aids in the identification of biological processes and molecular-level function understanding of the proteins across species. Through topological and biological similarity, network alignment offers a means of identifying comparable sections across various species and can facilitate the transmission of biological knowledge between them. Several strategies for network alignment have been developed, but it is still difficult to achieve high AFS (Average Functional Similarity) and Coverage. Moreover, the topological methods did not produce quality alignments in terms of average functional similarity. Similarly, the biological information did not guarantee high topological performance. This thesis presents a PPI network alignment algorithm and reviews the existing studies (in terms of semantic similarity and coverage). This thesis investigates different topological measures in addition to biological information to improve the topological and biological performance of the aligners. This thesis presents a novel topological approach that maximizes the AFS, coverage, and overall topological quality. Dr. Ansar Siddique Fri, 01 Nov 2024 00:00:00 GMT http://hdl.handle.net/123456789/21042 2024-11-01T00:00:00Z http://hdl.handle.net/123456789/20888 Human Centered Design and Evaluation of AI Application in Medical Health 03-243222-001, Amna Faisal Through this research study, the study aims to facilitate AI-based medical health applications both local and international, more accessible and useable to semi-literate users of Pakistan. At the initial stage, systematic literature review (SLR) identified critical factors regarding AI-based medical health applications and semiliterate users. Twelve factors are identified within three categories: Usability, User Experience, and Content & Design. These factors included Ease of Navigation, Memorability, Learnability, Efficiency, Effectiveness, Accessibility, User Satisfaction, Interactivity, Readability, Ease of Use, Content, and Design. MCDM techniques like AHP and Fuzzy AHP are applied to prioritize the factors. From the results, the top six significant factors are selected for further analysis; these are Ease of Navigation, Learnability, Effectiveness, User Satisfaction, Memorability, and Readability. Then, a further SLR is conducted for choosing applications, which include ratings from the application, downloaded statistics, presence of AI features, and user reviews. Next, two validation methods are applied for this research. First, we will consider the match of 10 selected AI-based health applications to prioritized factors with expert assistance using the Google Form survey. Applications in compliance with these factors are further selected for the next method: heuristic evaluation. With this heuristic evaluation, experts draw attention to several usability issues and provide recommendations to enhance the applications' usability. Among the selected three applications that are WebMD, Healthwire, and Dawaai, least user experiences were satisfied through these applications, and for this reason, they are selected in the next phase of heuristic evaluation. A prototype is created with the feedback, which included all the changes it planned to make based on the issues. The same experts reviewed this prototype to check whether the new design met their expectations and whether they fully agreed with the proposed improvements. This iterative process highlights a need for AI-based health applications to be culturally sensitive and accessible, especially for semi-literate users in Pakistan. The research focuses on critical usability factors and iterates the design based on expert feedback. The improvement achieved is in terms of usability, user experience, and accessibility of AI-based health applications to cater to diverse populations more effectively. Dr. Abdul Hafeez Fri, 01 Nov 2024 00:00:00 GMT http://hdl.handle.net/123456789/20888 2024-11-01T00:00:00Z