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3D Carrom board game
(UMT, Lahore, 2018) RIZWAN AHSAN, MUHAMMAD BILAL and SHAHEHRYAR BAIG
It is a 3D game called 3D Carrom Board that is developed in Unity. Background graphics are implemented in blender. All the storyboards are attached to this document in Data Flow Diagram section for your consideration. This game will be played on an online platform and can be logged in via face-book as well. Multiplayer’s can engage themselves in a group of two or one-on-one contestants can also be entertained. The players will communicate through a chat box. Participants can suggest players to one another. Moreover, the aspects explored are the skills and techniques required to be successful in the game, the environment that skaters skate in, the personal vs. group identity that is shown through the general appearance of the skater, and the values and icons that the game teaches players.
3D Virtual tour of umt
(UMT, Lahore, 2025) Mahnoor Fatima, Ayesha Ejaz and Tashbeeb Khadim
The 3D Virtual Tour of UMT (University of Management and Technology) is a forward-thinking and technologically advanced final year project designed to revolutionize the way users experience and explore university campuses. This project aims to create an immersive, interactive, and user-friendly virtual environment that allows prospective students, parents, faculty, alumni, and other visitors to remotely navigate and explore the UMT campus from anywhere in the world. Utilizing state-of-the-art tools and platforms such as Unity 3D, this virtual tour will simulate a realistic 3D environment that replicates the actual infrastructure and ambiance of the university. Key features of the tour include lifelike 3D models of various university facilities, such as lecture halls, libraries, laboratories, cafeterias, administrative offices, and recreational areas. These spaces will be accurately modeled and textured to give users a visually rich and true-to-life experience. The primary objective of the project is to provide an engaging and informative platform that enables users to explore the campus at their own pace, gain valuable insights into the university’s offerings, and make informed decisions—especially for those who are unable to visit in person. Additionally, the project supports the university’s marketing and outreach efforts by showcasing the campus infrastructure and academic environment in a modern, interactive format. By integrating elements like smooth navigation, clickable hotspots for additional information, and potentially voice-over or guided tours, this virtual experience is designed to be both educational and enjoyable. This project not only demonstrates the practical application of computer graphics, virtual systems, and software engineering principles but also reflects a strong commitment to enhancing accessibility and user experience in the educational domain. In summary, the 3D Virtual Tour of UMT combines advanced visualization technology with real-world practicality to create a compelling digital solution that bridges the gap between physical presence and virtual exploration
A bibliometric analysis of publications in flagship conference for database management system research
(UMT, Lahore, 2020) SUMBAL NAWAZ
The database management system is an important research field in electrical engineering, computer science, and computer engineering. From the previous hundreds of years, the development and research in this field have been increasingly growing around the globe. In this bibliometric study, the data of two conferences SIGMOD (Special Interest Group on Management of Data) and PODS (Principles of Database Systems) are taken. The data are downloaded from the SIGMOD and PODS website. This study worked on 10 years (2010-2019) data to address bibliometric questions in these conferences. SIGMOD conference began in 1975 and it specializes in the research area of databases and data management problems. While PODS is the research conference that specializes in research related to database theory. Since 1982 Pods has been done yearly and in 1991 this conference collaborated with SIGMOD. The literature of the database management system consists of three types of analysis 1) metadata analysis 2) content-based analysis and 3) citation-based analysis. This study identified the consequential research trends of the database management system in the last ten years. Furthermore, it also looked at the most leading institutes, countries, and authorities based on article citation and publication count. In this study, a methodology is proposed for performing the extensive bibliometric analysis on the database management system. For all I know, until now there is no such study exists in the area of the database management system.
A bibliometric analysis of publications in leading journals for database management system research
(UMT, Lahore, 2020) KALSOOM FATIMA
The database management system is a significant research field in Electrical Engineering and Computer Science. For last hundreds of years, the field is increasingly growing in terms of development and research throughout the world. In this study, the researcher utilized two important database management systems such as Institute of Electrical and Electronics Engineers (IEEE) and Association for Computing Machinery (ACM) that is obtained from ACM, Cross Ref, Scopus, IEEE Xplore, for ten years of time span (2010-2019) to understand bibliometric questions. All venues publish distinct research. IEEE and ACM are greatly reputed journal fields. ACM publishes unique research and it is also an expanded version of distinct research and it has the same genre. IEEE publishes reviews and surveys. This study not only tells us about the past work but also informs us about the future works. In this Bibliometric paper, the researcher planned to follow the co. evolution pattern in IEEE and ACM journals. In this paper, investigation of database management system consists of (a) metadata analysis (b) content- based analysis (c) citation-based analysis. Furthermore, this study recognizes the important patterns, authors, countries and foundations that depend on article citations and publication count. With the help of this the study, the researcher is introducing a system and methodology for providing a bibliometric investigation on data base management system. The researcher’s stance is that no study has been attempted in the database management system before this research.
A BLOCKCHAIN BASED FRAMEWORK FOR POTATO SEED TRACEABILITY IN AGRICULTURE SUPPLY CHAIN MANAGEMENT
(UMT, Lahore, 2023) FATIMA NAWAZ
A CIPHER PROPOSAL BASED ON A NOVEL TRIGONOMETRIC CHAOTIC MAP
(UMT, Lahore, 2023) UMER NAWAZ
In the ever-evolving digital era, the sharing of information has become commonplace across numerous platforms and channels. However, this widespread exchange also exposes data to potential vulnerabilities and threats. To counteract such risks, the implementation of a cipher algorithm becomes imperative. Modern ciphers utilize substitution boxes to introduce chaos and fortify data security. In this study, a unique substitution box is constructed utilizing trigonometric functions. To assess the effectiveness of the substitution box, its properties are thoroughly evaluated. Additionally, a novel block cipher is introduced, utilizing the substitution box to encrypt 128 bits of data. The cipher consists of 16 encryption rounds and necessitates a 128-bit key for executing multiple operations. Each round involves the generation of a distinct key for encryption purposes. Comprising four primary functions and a pre-function, the cipher incorporates the designed substitution box to offer substantial complexity, thus ensuring a high level of encryption standard through its chaotic behavior. To validate the efficiency of the proposed cipher, the constructed substitution box undergoes meticulous evaluation using various metrics, while the cipher itself is extensively tested. The obtained results unequivocally demonstrate that the proposed cipher outperforms traditional ciphers, significantly enhancing data security. Furthermore, the proposed cipher exhibits scalability and can be tailored to specific requirements, thereby offering versatility in its application.
A comparative analysis of responsible AI for digital health
(UMT, Lahore, 2021) AHSAN SABIR
Artificial Intelligence (AI) is increasingly utilized for decision-making and automation tasks in all sectors, public sectors, and even law. Responsible AI is focused on developing, implementing, and applying transparent, accountable ethical AI technology to eliminate biases, encourage equality fairness, and improve the interpretability and the explanation of outcomes. First, the Review briefly explains the literature on Responsible AI. Then, this paper reviews the principles-to-practices gap. We offer five possible explanations that explain this gap, ranging from a disciplinary division and an abundance of tools. In turn, we argue that a broad, operationalizable, flexible, iterative, guided, and participatory impact assessment framework is a promising approach to close the principles-to-practices gap. Finally, A thorough discussion of the case study "Impact Assessments to Help Support Responsible AI in Forest Ecosystem Restoration." To assist practitioners in implementing these guidelines, we look at the results of several interviews; the Responsible use of AI is discussed briefly.
A CYBERBULLYING MULTICLASS CLASSIFICATION WITH AN ATTENTION MECHANISM HANDCRAFTED CNNBERT WITH BILSTM
(UMT, Lahore, 2023) Ammara Asghar
Cyberbullying is an important problem that affects people across the globe and is unpleasant for both the individuals who are bullied and society overall. One type of bullying that occurs on several social networking platforms is referred to as cyberbullying. Twitter has become one of the most well-known locales for posting recordings, with a great many tweets. AI and normal language handling procedures are utilized to investigate the remarks. However, for cyberbullying analysis, many machine learning models analyze comments in fixed-length frames or sequences, which results in the loss of long-term context information. This postulation explores an original technique for recognizing cyberbullying that consolidates Convolutional Brain Organizations with Consideration System BERT (CNNBERT) and Bi-directional Long Momentary Memory (BiLSTM) models. This exploration utilizes a half and half profound learning way to deal with create a complex model that can precisely distinguish and characterize cyberbullying content across online entertainment stages. The proposed technique performs effectively by evaluating text-based data to identify complex instances of cyberbullying. Combining BiLSTM expert ability to process sequential input with the attention mechanism of BERT and CNN, which enhances feature extraction.
A data partitioning scheme for secure data storage in cloud environments
(UMT, Lahore, 2021) SYED MUHAMMAD ALI RAZA
Data is being produced at very fast rate due to incremental use of social media, web services, mobile devices, cyber-physical systems and Internet of Things (IoT). Storing such huge data on local storage is not feasible for all the users due to certain issues like cost, maintenance, backup etc. Cloud Computing (CC) provides unlimited resources at minimal cost. However, storing the data in remote location raises privacy threats to data. Although the cloud service provider (CSP) offers mechanism to secure the data from outside attacks but there is still a risk of inside attacks on the data. To prevent such attacks Wang at al.(T. Wang et al., 2018) proposed a three-layered architecture by using fog computing. Although the scheme ensured the confidentiality of the data but data recovery mechanism was not provided. Data modification at different places without the knowledge of the data owner is also an issue. To address such issues, we proposed a data-partitioning scheme with recovery mechanism for secure storage of data in the multi-cloud environment. Simulation results depicted that decoding and encoding speeds of the proposed solution is 40% and 17 % faster than the existing techniques, respectively. Similarly, data recovery mechanism takes 60% less processing time then the existing technique.
A DEEP LEARNING MODEL FOR SKIN CANCER, MONKEYPOX, AND CHICKENPOX IDENTIFICATION
(UMT, Lahore, 2024) MUHAMMAD TAYYAB RAFIQUE
In health care, correct assessment of skin conditions like skin cancer, chickenpox, and monkeypox is essential because early identification greatly impacts treatment outcomes. Conventional techniques for identifying skin conditions can be time-consuming, capable of making mistakes, and dependent on human skill, highlighting the need for more accurate and efficient instruments. Deep learning and computer vision are used in this work to address these issues. To be more exact, we distinguish between the three distinct skin conditions using the advanced Vision Transformer (ViT) model. The results of this model's remarkable; with an accuracy rate of 93.54%, it outperforms models like VGG16, MobileNetV2, ResNet152V2, and DenseNet201. These developments extend the reach of computer vision methods in the domain of medical imaging to provide useful applications for accurate and consistent tumor identification in equally extensive and medical care. This method reduces the need for separate deep-learning models and increases analytical efficiency by exemplifying that a single deep-learning model can handle a range of skin conditions. The study's results show how artificial intelligence may improve measurement accuracy and assist medical practitioners in making prompt, accurate decisions. The proposed model may be commonly applied in care coordination as it provides a scalable method for the early detection and management of severe skin problems.
A framework to secure health monitoring applications on android platform
(UMT, Lahore, 2019) SOBIA MEHRBAN
Smartphones in combination with medical software have made documentations, record keeping, have access to information and inter-personnel communication which is faster and more effective in the field of mobile health. Healthcare monitoring applications(HMA) are the applications of software and its work at the level of hardware, as HMA are mostly used to sort out chronic diseases, monitor the elder and expectant mothers, and help the patients to schedule the medicine time, and provide effective and efficient service to the patients, doctors and caretakers. Whereas, many operating system are present in smartphones nowadays, however, Android is the most popular operating system and it handled all the sensitive data that are useful for the professional (doctor, physician, and consultant) over unsecured internet communication and hacker’s server. Due to this, treatment involves basic privacy issues regarding different data of patients, doctors and caregivers. As security in health monitoring apps is necessary to keep the patient’s records confidential, however, their data can be exchanged with wireless networks, and this is the main threat in health monitoring applications. Thus, the important goal is to provide proper security for the patient’s data from unauthorized people who actually misuse the health records. The major concern is to protect all the patient’s sensitive data from unauthorized third parties. In this thesis a secure real time framework has been presented which will help to secure the medical data related with Android health monitoring apps. Through this framework the medical data can be stored properly, protected from threats and improved records monitoring abilities. The proposed security framework has been designed consisting of two layers; one is, A Monitoring Layer (ML) which is used to enforce the modules of security-checks, and second is, an Interface Layer (IL) which provide interface between Monitoring Layer and Android Operating System. ML implements different polices at several levels of android platform through IL. The validation of framework is shown through the implementation of actual Android device, and this implementation shows the framework and evaluate its performance practically. Different experiments have been performed to check the efficiency and effectiveness of proposed framework, and the results of experiments have successfully protect the health monitoring applications against different attacks.
A new cipher design based on trigonometric dynamic S-Box
(UMT, Lahore, 2022) SYEDA UM-E-RUBAB TIRMAZI
An information security system is a vital need for the security of data. Strong cryptographic ciphers are required to ensure the comprehensive performance of information security systems. Ciphers are mathematical algorithms that convert plaintext into ciphertext and provide security to the data. Cipher uses different operations like XOR, shift, permutation operation, substitution operation, and many more. For any cipher, the substitution box plays a key role in strong security performance parameters. An S-Box is utilized in a security system that is based on the block ciphering technique, as it is the core element for providing non-linearity in a cryptosystem. In our study, we proposed an S-Box construction method for generating dynamic S-Box with cryptographically strong properties. The idea proposed in this study is inspired by chaotic maps. The proposed dynamic 8*8 S-Box is analyzed using the strict avalanche criterion (SAC), bit independence criterion (BIC), non-linearity, and differential uniformity. The proposed method for the construction of S-Box was proved to produce S-Boxes with high non-linearity and randomness characteristics and the proposed cipher is secure. The proposed cipher consists of ten rounds for encryption, decryption, and key generation. The master key used is 128 bits. The operations used in the proposed cipher are XOR operation, substitution operation, complement, row transformation, etc.
A NOVEL CHAOTIC MAP-BASED S-BOX AND ITS APPLICATION IN CIPHER DESIGN
(UMT, Lahore, 2024) YAHYA TAUQEER BHATTI
Data has become the main asset, which create more threats for individual to large networks. Providing security to data is essential and for this purpose, ciphers of different techniques are being developed. To enhance data security, guides are planned and applied and being developed of these ciphers. Existing chaotic maps lack randomness generation capabilities which is a vital requirement of contemporary cipher. Current ciphers use a substitution box (S-Box) as a center module for information security. In this thesis, a novel chaotic map is proposed for the plan of new unique S-Box. Models like Differential Approximation Probability (DP), Linear Approximation Probability (LP), Bit Independence Criterion (BIC), Strict Avalanche Criterion (SAC), Nonlinearity (NL), and Bijectiveness are applied to examine the proposed S-Box. Once the dynamic S-Box is generated, it is integrated into a novel cipher developed as part of this study. During each cipher round, a new S-Box is created using values derived from the key, making it harder for attackers to breach the system. The comparative analysis between recent S-Boxes is used to ensure the true potential of the proposed S-Box‘s data security
A novel cipher design based on a new chaotic s-box
(UMT, Lahore, 2025) Taha Muhammad Shafiq, Muhammad Ayaan, Asmara Shaukat and Ahmad
This project highlights the design of a secure cryptosystem that integrates a Substitution Box (S-Box) obtained from algebraic structure and chaotic map. The main goal of this project is to develop a S-Box that offer enhanced cryptographic properties like non-linearity, and strong resistance to cryptanalysis to enhance the security of data encryption system. By employing a range of cryptographic methods, the S-Box is integrated into a block cipher design to enhance the security of a system. A thorough evaluation is conducted to assess the performance of the cryptosystem, with specific focus on metrics like non-linearity, the avalanche effect, bit independence, and resistance to potential cryptographic attacks. A more robust and secure encryption framework is proposed in this project, which will participate in the sector of cryptography. The proposed cryptosystem has the potential to be applied in various domains such as secure communication, data protection, and encryption for sensitive sectors like healthcare, and financial services. The development of this enhanced cryptographic framework could pave the way for more scalable, adaptable, and secure encryption solutions, making it a valuable contribution to the ongoing evolution of data protection and cryptographic research.
A novel cipher design using an innovative chaotic map-based S-BOX
(UMT, Lahore, 2021) ANNAS WASIM MALIK
In the present-day world, secure communication of sensitive information between different entities is a major challenge. Hence, many cryptosystems have been developed for this purpose. Various operations are used in these cryptosystems. The most common operations used are substitution and permutation. The operation of substitution is performed by the substitution-box (S-box), which is a look-up table that takes m bits as input and replaces them with n bits. The construction of an S-box with strong cryptographic features is an essential part of building a robust and secure cryptosystem. Many researchers have developed complex techniques to construct S-boxes. In this research, a novel approach has been adopted to design a compound-chaotic map-based robust and dynamic S-box. The proposed methodology for constructing the S-box is simple and has good strength. The proposed S-Box is thoroughly examined and tested for cryptographic strength using a variety of standard criteria, including Non-Linearity (NL), Strict Avalanche Criterion (SAC), Bit Independence Criterion (BIC), Linear Probability (LP), and Differential Probability (DP). The results of the proposed S-box have been compared to those of others, demonstrating that it is cryptographically sound and worthy of inclusion in modern cryptosystems.
A NOVEL CUBIC CHAOTIC MAP FOR SBOX DESIGN AND CIPHER
(UMT, Lahore, 2023) MUHAMMAD AHMAD
The rapid advancements in technology have resulted in a significant rise in the quantity of data being shared and transmitted across various sources on the network. With data being a critical aspect of modern life, its importance cannot be overstated. Unfortunately, this also makes it easier for hackers to gain access to and steal sensitive information. To maintain data security and protect against such threats, secure connections are essential. Cryptography is employed to enhance the security of private data and communication networks, utilizing various substitution and permutation strategies. Many researchers have developed algorithms using dynamic substitution boxes to bolster security against attackers. However, since some of these S-boxes are weak and ineffective, there is a constant need for strong S-Boxes. In this thesis, a robust key-based substitution box (S-Box) based on the 1-D chaotic method is proposed and its strength using standard benchmarks has been evaluated. The results are promising that indicate that the proposed S-Box could be utilized in modern ciphers with confidence. A new cipher has been designed to use the proposed S-Box to provide security of data. The proposed cipher has eight rounds and each round is performing the same operations. The obtained results of our proposed S-Box are comparatively worthy. The cipher's proposed plaintext size is 256 bits, while the key length is 256 bits.
A novel method of dynamic S-BOX design using inverse linear transformation and permutation
(UMT, Lahore, 2020) JAHAN ZAIB
In data security, block ciphers are notable for proficient strategies for protecting the information. It comprises of replacements and changes. In symmetric and asymmetric ciphers, a few Substitution Boxes (S-Box) are utilized for replacement purposes. Earlier designed S-Boxes utilized various differing procedures shaping complex calculations, however, they lack strength. In this research work, a novel method is proposed utilizing Inverse Linear Transformation and Permutation to produce Substitution Box. The proposed S-Box is less perplexing, compelling, and end up being higher in quality than different existing S-Boxes. Different perspectives like Linear Probability, Differential Probability, Strict Avalanche Criterion, Bit Independent Criterion, and Non-Linearity are utilized to estimate the strength of proposed S-Box. To check the proposed S-Box strength, the values of the proposed S-Box properties are compared with the pre-designed S-Boxes. The comparison validates that the new method is significantly more effective for utilization in the block ciphers.
A novel synthetization of dynamic S-BOX using a radical chaotic map
(UMT, Lahore, 2023) MUHAMMAD FAHAD ASAD
In the endeavor of information security, we often rely heavily on encryption algorithms to safeguard data from data thieves. In the designs that enable us to protect data, we attribute the S-Box as the sole nonlinear factor responsible for guaranteeing a strong algorithm compared to a weaker algorithm. But achieving a strong and functional technique for synthesizing a well preforming S-Box, is the real struggle many authors and fellow cryptographers are challenged against. In this work, a strong S-Box Synthesizing technique is proposed using radical-based chaotic map of 6 irregular intervals. Upon investigating its performance being up to the required standard of modern encryptions, it has been put against and analyzed through standard tests, scores compared with notable existing works and substantiated results have been highlighted. The results thus far have proved that the proposed work in this literature holds up to its promise of synthesizing a strong and resilient S-Box.
A RADICAL CHAOTIC MAP-BASED S-BOX AND ITS APPLICATION IN A NEW CIPHER
(UMT, Lahore, 2023) KAINAAT MALIK
The broad usage of communication technology nowadays suggests the necessity for certain security measures to be put in place. Different ciphers are being created to do this by utilizing numerous strategies. These ciphers are currently being created using chaotic maps. Using chaos in cryptographic algorithms has the advantage of making data more unstable and inconsistent. A substitution box is a unique and nonlinear essential component of block ciphers. To fend off modern attacks, extremely diffusive S-Boxes are required as nonlinear confusion sublayers in cryptosystems. By adopting better substitution box design approaches, a ciphertext of higher quality can be made. In this research, a novel chaotic map is suggested and used to create a new S-Box. A new cipher is created employing the suggested S-Box and a new function. Along with the decryption procedure, the key generation approach for the cipher is also described. Nonlinearity (NL), Linear Approximation Probability (LP), Differential Approximation Probability (DP), Strict Avalanche Criteria (SAC), and Bit Independence Criteria (BIC) are among the parameters taken into consideration when analyzing and evaluating the proposed S-Box's effectiveness against various cyberattacks. The results of the performance test show that the suggested S-Box has strong cryptographic characteristics, is resistant to several attacks and is appropriate for use in modern ciphers.
A SIMULTANEOUS FEATURE SELECTION AND MALWARE DETECTION USING CONTRASTIVE LEARNING
(UMT, Lahore, 2025) RABIA NOREEN
The fast pace at which advanced, evasive and polymorphic malware are being created presents a great threat to the traditional security infrastructure that are based on static signature detection techniques. In this research paper, a new, smart, and modular architecture of detecting malware is presented, which is based on the combination of contrastive representation learning, automated feature selection, and deep ensemble modeling which provide robust and scalable malware detection and context-aware malware identification across diverse digital ecosystems. The proposed framework attempts at estimating research quality with the use of a multi-stage pipeline including a data preprocessing stage with rigid parameters, such as statistical imputation, Min-Max, Z-score normalization, and SMOTE-based class rebalancing, to improve the integrity and representation of research quality. With the help of contrastive learning, attributed to InfoNCE, the system learns extremely discriminative features by minimizing similarity measures between positive and negative pairs of samples, and can effectively discriminate between benign and malicious activities. High dimensionality feature spaces are reduced to be easily interpreted and to eliminate noise by means of Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE). A large variety of machine learning and deep learning algorithms such as Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees, Random Forest, XGBoost, Convolutional Neural Networks (CNNs), and the conv1D-LSTM architecture, are trained and optimized using an exhaustive grid- based and random grid search as well as a k-fold cross-validation of the trained models and ensemble techniques, such as voting, stacking, to guarantee high stability in predictions. The four different heterogeneous datasets over the four memories based, visual, document-centric and network- layer threats used are Obfuscated Malware, Image Malware, CIC-Evasive-PDFMal2022 and UNSW-NB15. The findings prove outstanding detection effectiveness, since ensemble classifiers (Random Forest and XGBoost) show perfect results of more than 99.9% accuracy, whereas Conv1D- LSTM model shows 97 percent accuracy with negligible values of testing loss on network traffic. The model performance should be measured by evaluation measures, such as precision, recall, F1-score, AUC-ROC, and confusion matrices that highlights the ability of the model to reduce false alarms to the minimum with the maximum level of sensitivity to the emergent threat detection. This study presents a practical state-of-the-art, real-time malware detection scheme that can be smoothly deployed in any high-stakes domain, including critical infrastructure, financial, healthcare, and other IoT-enabled networks. Its performance ability to constantly update and learn, the ability to discriminate at a fine-grained level, and its capability of application across domains is a very important step towards intelligent cybersecurity defense.

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