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Mitigating Adversarial and Data Poisoning Attacks in Cybersecurity Applications
Saad Rustam;Usama Arshad;Muhammad Ahmad;Najam Sawera;Hammad Ali
2024 19th International Conference on Emerging Technologies (ICET)
Year: 2024 | Conference Paper |
The integration of deep learning models in cybersecurity applications, such as malware detection, intrusion detection, and spam filtering, has introduced significant vulner-abilities to security threats. Among these, adversarial and data poisoning attacks pose particularly severe risks, compromising the reliability and accuracy of learning algorithms. Adversarial attacks involve the manipulation o...Show More
VigilantAI: Real-time detection of anomalous activity from a video stream using deep learning
Danish Javed;Usama Arshad;Shuhrabeel Peerzada;Muhammad Ramiz Saud;Nisar Ali;Raja Hashim Ali
2024 International Conference on IT and Industrial Technologies (ICIT)
Year: 2024 | Conference Paper |
In an era where artificial intelligence (AI) solutions are increasingly integrated into various sectors, we have utilized Artificial Intelligence for enhancing public safety through real-time detection of illegal activities such as robberies and threats at gunpoint using CCTV footage. With the proliferation of deep learning in object detection, the study focuses on deploying the YoloV5 model, trai...Show More
Genetic ledger – pioneering secure and authentic data storage through DNA and blockchain
International Conference on AI and the Digital Economy (CADE 2024)
Year: 2024 | Conference Paper |
The rapid increase in digital data necessitates advanced storage solutions. This paper introduces the DNA-chain system, an innovative integration of DNA data storage and blockchain technology. Our hybrid system utilizes the high-density storage capability of synthetic DNA to handle massive amounts of data, alongside blockchain’s robust security and traceability features. We detail techniques for e...Show More
Context-Aware Audio-Visual Speech Enhancement Based on Neuro-Fuzzy Modeling and User Preference Learning
Song Chen;Jasper Kirton-Wingate;Faiyaz Doctor;Usama Arshad;Kia Dashtipour;Mandar Gogate;Zahid Halim;Ahmed Al-Dubai;Tughrul Arslan;Amir Hussain
It is estimated that by 2050 approximately one in ten individuals globally will experience disabling hearing impairment. In the presence of everyday reverberant noise, a substantial proportion of individual users encounter challenges in speech comprehension. This study introduces a novel application of neuro-fuzzy modeling that synergizes and fuses audio-visual speech enhancement (AV SE) with an i...Show More
Influencing Factors in Facial Recognition and Verification: A Siamese Neural Network Hyperparameter Study
2024 International Conference on Engineering & Computing Technologies (ICECT)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
There are many practical uses for facial verification and recognition jobs. In this study, we look into the developments and difficulties faced in facial recognition technologies, especially in the application of Siamese neural networks. This study converges around optimizing hyperparameters to improve Siamese network accuracy and performance. We evaluated the impact of different optimizers, loss ...Show More
Incentivizing Recycling Behavior: Repro - A Reward Based CNN-Enabled Framework for Smart Waste Collection
2024 International Conference on Engineering & Computing Technologies (ICECT)
Year: 2024 | Conference Paper |
Efficient waste management together with certain measures to promote the recycling process are essential for over-coming global environmental issues. In this study, we describe Repro an app, which is targeted at transforming how we collect waste and recycle by integrating CNN technology and a reward-based system. Users, with the Repro app, take pictures of any trash they discard and rely on the un...Show More
Harnessing Long Short Term Memory Networks for Stock Market Forecasts
2024 International Conference on Engineering & Computing Technologies (ICECT)
Year: 2024 | Conference Paper |
Stock market trend prediction is a classical re-gression problem with effects on stock exchange. This research is presenting a detailed exploration of how Long Short Term Memory (LSTM) networks may be used for predicting stock market trends. The LSTM model was employed instead of the conventional Recurrent Neural Networks (RNNs), to learn from historical financial data. The study examines the oper...Show More
Futuristic Decentralized Vehicular Network Architecture and Repairing Management System on Blockchain
IEEE Internet of Things Journal
Cited by: Papers (1)
Blockchain technology is used often as a merger with other technologies to achieve a high level of security, privacy, and robustness and to handle issues, such as maliciousness of nodes, privacy leakage, the selfishness of nodes, communication delays, and high execution and transaction costs. There is currently a lack of a comprehensive system for automating and cost-effectively managing vehicle r...Show More
Leveraging AI and NLP in Chatbot Development: An Experimental Study
Abdul Wahab Paracha;Usama Arshad;Raja Hashim Ali;Zain Ul Abideen;Muhammad Huzaifa Shah;Talha Ali Khan;Ali Zeeshan Ijaz;Nisar Ali;Abu Bakar Siddique
2023 International Conference on Frontiers of Information Technology (FIT)
Year: 2023 | Conference Paper |
Cited by: Papers (7)
In the current era of chatbots, this research delves into the advancements in AI chatbots, drawing on artificial intelligence (AI) and natural language processing (NLP) techniques to mimic human-like conversations. A particular focus is given to the potential of chatbots in facilitating multitasking dialogues, offering emotional support, and addressing complex subject matter, all the while respect...Show More
Mitigating Crop Losses: AI-enabled Disease Detection in Tomato Plants
Hassan Ashfaq;Usama Arshad;Raja Hashim Ali;Zain ul Abideen;Muhammad Huzaifa Shah;Talha Ali Khan;Ali Zeeshan Ijaz;Nisar Ali;Abu Bakar Siddique
2023 International Conference on Frontiers of Information Technology (FIT)
Year: 2023 | Conference Paper |
Cited by: Papers (13)
Fostering crop health is vital for global food security, underscoring the need for effective disease detection. This research introduces an innovative artificial intelligence (AI) model designed to enhance the detection and diagnosis of diseases in tomato plants, particularly focusing on Early Blight and Late Blight. Significantly, our model leverages cutting-edge image processing techniques to im...Show More
Robust and Reliable Liveness Detection Models for Facial Recognition Systems
Haris Anjum;Usama Arshad;Raja Hashim Ali;Zain Ul Abideen;Muhammad Huzaifa Shah;Talha Ali Khan;Ali Zeeshan Ijaz;Abu Bakar Siddique;Muhammad Imad
2023 International Conference on Frontiers of Information Technology (FIT)
Year: 2023 | Conference Paper |
Cited by: Papers (8)
Facial Liveness Detection is instrumental in combating fraudulent practices and identity theft by differentiating genuine faces from forgeries. Given that facial recognition is now an integral part of many sectors like banking and law enforcement, liveness detection has become a vital aspect for maintaining the trustworthiness of these applications. Unfortunately, the utility of prevalent facial l...Show More
Exploiting Partial Observability and Optimized Simple State Representations in Deep Q-Learning
Danyaal Mahmood;Usama Arshad;Raja Hashim Ali;Zain Ul Abideen;Muhammad Huzaifa Shah;Talha Ali Khan;Ali Zeeshan Ijaz;Nisar Ali;Abu Bakar Siddique
2023 International Conference on Frontiers of Information Technology (FIT)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
The advent of deep Q-learning has opened up new possibilities in training autonomous agents to perform intelligently in intricate settings. This research work examines the potential of deep Q-learning in the paradigmatic Snake game, which requires an agent to navigate a grid, ingest food items, and avoid collisions. We incorporated a partially observable game state, introducing a novel level of di...Show More
Performance Evaluation of Popular Deep Neural Networks for Neural Machine Translation
Muhammad Naeem;Abu Bakar Siddique;Raja Hashim Ali;Usama Arshad;Zain ul Abideen;Talha Ali Khan;Muhammad Huzaifa Shah;Ali Zeeshan Ijaz;Nisar Ali
2023 International Conference on Frontiers of Information Technology (FIT)
Year: 2023 | Conference Paper |
Cited by: Papers (10)
The field of Neural Machine Translation (NMT) has shown impressive performance for quick and easy communication in various languages spoken all over the world. NMT helps us by improving communication between different languages. For this purpose, different sequential models are used such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU). Analysis amo...Show More
A New Rhythm in A1: Convolutional Neural Networks for Music Genre Classification
2023 18th International Conference on Emerging Technologies (ICET)
Year: 2023 | Conference Paper |
Cited by: Papers (14)
Despite the significance of music genre classification in audio identification, it remains under-explored within AI research. This tool is crucial for personalized music recommendations and similar music detection. We have developed an efficient AI model that leverages Convolutional Neural Networks (CNNs), offering high-precision genre identification when integrated into a graphical user interface...Show More
Identifying COVID-19 through X-ray and CT scan images using Machine Learning
Maham Shehzadi;Usama Arshad;Zainul Abideen;Raja Hashim Ali;Ammad Aslam Khan;Ali Zeeshan Ijaz
2023 18th International Conference on Emerging Technologies (ICET)
Year: 2023 | Conference Paper |
Cited by: Papers (13)
In the current global scenario, marked by the COVID-19 pandemic, it has become imperative for healthcare systems around the globe to swiftly and accurately diagnose the disease. This is where cutting-edge approaches such as machine learning (ML) come into play, specifically, ML-based identification of COVID-19 from chest X-ray and CT-Scan imagery. Our research collective is devotedly working towar...Show More
AdaptiveCloset: Reinforcement Learning in Personalized Clothing Recommendations
Omar Kashif Majeed;Zain ul Abideen;Usama Arshad;Raja Hashim Ali;Abdullah Habib;Rafay Mustafa
2023 18th International Conference on Emerging Technologies (ICET)
Year: 2023 | Conference Paper |
Cited by: Papers (14)
The fashion industry, with its myriad choices, often overwhelms consumers. Addressing this, AdaptiveCloset introduces a groundbreaking approach to tailoring clothing suggestions by harnessing the power of reinforcement learning (RL). Unlike conventional AI methodologies in a fashion that merely suggests based on past preferences, our system dynamically ad-justs using realtime user feedback, ensuri...Show More
Innovative Poverty Estimation through Machine Learning Approaches
Ahmad Mashhood;Zain ul Abideen;Usama Arshad;Raja Hashim Ali;Ammad Aslam Khan;Bahram Khan
2023 18th International Conference on Emerging Technologies (ICET)
Year: 2023 | Conference Paper |
Cited by: Papers (14)
This paper delves into the application and capabilities of machine learning methodologies in forecasting poverty scenarios, underlining the importance of varied data sources, along with the interpretability and explainability of models to refine the precision and transparency of poverty prediction mechanisms. It primarily utilizes an iteration of the LightGBM algorithm to infer poverty stages prem...Show More
An Enhanced Genetic Algorithm Framework for Efficient Solutions to Capacitated Vehicle Routing Problems
Usama Arshad;Hamza Hasan Ellahie;Zain ul Abideen;Raja Hashim Ali
2023 18th International Conference on Emerging Technologies (ICET)
Year: 2023 | Conference Paper |
Cited by: Papers (15)
Capacitated Vehicle Routing Problems (CVRPs), a widely acknowledged NP-hard issue, pertains to the optimal routing of a limited-capacity vehicle fleet to fulfill customer demand, aiming for the least possible travel distance or cost. Despite the presence of numerous heuristic and exact approaches, the combinatorial characteristic of CVRP renders it challenging, especially for large-scale instances...Show More
Revolutionizing Campus Exploration with GikiLenS: A Deep Learning-Powered Object Detection App
2023 18th International Conference on Emerging Technologies (ICET)
Year: 2023 | Conference Paper |
Cited by: Papers (14)
For newcomers and tourists, navigating university campuses can be difficult, resulting in aggravation and lost time. We respond by introducing “GikiLenS”, an object identification application driven by deep learning that revolutionizes campus exploration by accurately identifying buildings and landmarks while enhancing user experience. GikiLenS is a comprehensive and user-friendly smartphone appli...Show More
Deciphering Faces: Enhancing Emotion Detection with Machine Learning Techniques
Muhammad Haseeb Ishaq;Rafay Mustafa;Usama Arshad;Zain ul Abideen;Raja Hashim Ali;Abdullah Habib
2023 18th International Conference on Emerging Technologies (ICET)
Year: 2023 | Conference Paper |
Cited by: Papers (13)
Facial emotion detection holds significant relevance across various domains, from psychology and marketing to education and security. Despite its importance, prevalent techniques often grapple with issues like low precision, susceptibility to lighting changes, obstructions, and distinct facial characteristics. Addressing these challenges, our research embarked on devising a robust and precise faci...Show More
The YOLOv8 Edge: Harnessing Custom Datasets for Superior Real-Time Detection
Tafreed Ahmed;Ahmad Maaz;Danyaal Mahmood;Zain ul Abideen;Usama Arshad;Raja Hashim Ali
2023 18th International Conference on Emerging Technologies (ICET)
Year: 2023 | Conference Paper |
Cited by: Papers (14)
YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Developing a custom object detection solution that can detect specific objects in real-time video streams has the potential to revolutionize various fields and has been the subject of extensive research. Although there have been advances in object detection, there is still ...Show More
Investigating novel machine learning based intrusion detection models for NSL-KDD data sets
Muhammad Huzaifa Shah;Muhammad Abu Bakar;Raja Hashim Ali;Zain Ul Abideen;Usama Arshad;Ali Zeeshan Ijaz;Nisar Ali;Muhammad Imad;Said Nabi
2023 International Conference on IT and Industrial Technologies (ICIT)
Year: 2023 | Conference Paper |
Cited by: Papers (11)
This study investigates the application of the Mutual Information (MI) feature selection technique to improve the accuracy of Machine Learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML models, namely Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) with different kernels (1st, ...Show More
Exploring The Potential of HMMs in Linguistics for Part of Speech (POS) Tagging
Mahnoor Iftikhar;Raja Hashim Ali;Memoona Saleem;Usama Arshad;Ali Zeeshan Ijaz;Nisar Ali;Muhammad Imad;Muhammad Abu Bakar;Ali Aftab
2023 International Conference on IT and Industrial Technologies (ICIT)
Year: 2023 | Conference Paper |
Cited by: Papers (13)
The exponential growth in the adoption of information and communication technologies has sparked a notable surge in the demand for Natural Language Processing (NLP) tools. The tagging/identification of Part-of-Speech (POS) is of utmost importance in numerous natural language processing applications, including information extraction, parsing, and machine translation. It entails the assignment of a ...Show More
Prediction of Polycystic Ovary Syndrome Using Genetic Algorithm-driven Feature Selection
Fatima Faridoon;Raja Hashim Ali;Zain Ul Abideen;Nazia Shahzadi;Ali Zeeshan Ijaz;Usama Arshad;Nisar Ali;Muhammad Imad;Said Nabi
2023 International Conference on IT and Industrial Technologies (ICIT)
Year: 2023 | Conference Paper |
Cited by: Papers (11)
PolyCystic Ovary Syndrome (PCOS) is a hormonal disorder frequently found in women of reproductive age having a significant impact on the cause of infertility. It is an endocrine condition characterized by abnormalities in female hormone levels and aberrant synthesis of male hormones. This syndrome causes ovarian malfunction, increasing the risk of miscarriage and infertility. PCOS has a wide range...Show More
Performance comparison of genetic algorithms with traditional search techniques on the N-Queen Problem
Omar Kashif Majeed;Raja Hashim Ali;Ali Zeeshan Ijaz;Nisar Ali;Usama Arshad;Muhammad Imad;Said Nabi;Javaria Tahir;Memoona Saleem
2023 International Conference on IT and Industrial Technologies (ICIT)
Year: 2023 | Conference Paper |
Cited by: Papers (6)
This paper presents an analysis of solving the N-Queen problem using a genetic algorithm and compares its performance with traditional search algorithms like breadth-first search (BFS) and depth-first search (DFS). The N-Queen problem is a famous problem in the field of artificial intelligence that has been studied in depth. The genetic algorithm is an optimization algorithm inspired by the proces...Show More
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