Computer Science
Permanent URI for this collectionhttps://dspace.summituniversity.edu.ng/handle/123456789/22
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Item DEVELOPMENT OF QUANTUM SECURE AI CHATBOT(2024) HUSSEIN SOLIHAT BIDEMIChabot continuously increase customer services through immediate 24/7 support, handling simple questions, and releasing human agents to more complicated questions, which ultimately improve the productivity and satisfaction of users. However, there is an increasing concern about intruders getting access to users’ personal details through chatbot. To address this potential privacy concerns due to the handling of sensitive user data. This study presents a quantum safe intelligent chatbot. The design of a quantum safe intelligent chat bot signifies intersection area between quantum computing technology, Artificial Intelligence and Cyber security. This study adopts quantum key distribution principle in combination with AES algorithm to ensure secure and reliable communication while using chatbots.Item THE USE OF AI TO DETECT DROWSINESS IN DRIVERS(2023) ADEYEMO, MUKTARDriver distraction and drowsiness are critical concerns leading to road accidents, causing significant harm in Malaysia and worldwide. This study addresses the pressing need for effective driver distraction detection systems to enhance road safety. We propose a comprehensive methodology comprising data preprocessing, data augmentation, and a novel Driver Drowsiness and Monitoring System. This system incorporates a lightweight model, EfficientNetB0, coupled with a Channel Attention (CA) mechanism, demonstrating superior performance. In the experimentation phase, we evaluate our model on two benchmark datasets: State Farm Distracted Driver Detection (SFD3) and AUC Distracted Driver (AUCD2). The results indicate that our model achieves remarkable accuracy, precision, recall, and F1-score, surpassing existing state-of-the-art models. Moreover, our model exhibits exceptional time efficiency, making it suitable for real-time applications and resource-constrained devices. This research contributes to mitigating distracted driving's adverse effects, ultimately reducing accidents and promoting safer driving practices. The proposed methodology and results showcase the potential for deploying such systems to enhance road safety and reduce road accidents.Item DEVELOPMENT OF AI ENHANCED DARK WEB DETECTION SYSTEM WITH QUANTUM CRYPTOGRAPHY(2024) MUKHTAR OLATUNDE DUNMOYEQuantum computing combined with machine learning provides an environment that allows implementing better security measures within that realm. This methodology provides cutting-edge Quantum Machine Learning algorithms that turn out to be useful in information security measures. Quantum computing is a method of computing based on the principles of quantum mechanics, which allows quite incredibly fast computation, way beyond the standard forms previously available, and new solutions in encryption for secure communications and data storage. In information security, quantum machine learning algorithms are trained using quantum computing, where the quantum computer is used in processing enormous datasets to discover trends and flag anomalies that cannot be explained. The paper surveys some of the QML methods, including quantum support vector machines, quantum neural networks, and quantum clustering algorithms, and how they perform better than traditional methods for intrusion detection, malware classification, and mode detection. This paper also addresses issues and prospects related to quantum machine learning, such as quantum cryptography, which involves robust quantum-resistant algorithms and encryption systems in the face of quantum. It finally pointed out quantum machine learning as a new avenue for cyber security in cyberspace where illegal cyber activities are rapidly evolving and quantum technologies are emergingItem ENERGY OPTIMIZATION IN SMART GRIDS WITH DEEP REINFORCEMENT LEARNING(2024) AJIKOBI MUBARAK DAMILOLAThe escalating complexity, uncertainty, and data volumes in energy systems have rendered conventional methods ineffective in addressing decision-making and control challenges. As a result, data-driven approaches have become a crucial focus area. Deep reinforcement learning (DRL) represents a significant breakthrough in data-driven technology, earning its reputation as a true form of artificial intelligence (AI). By combining the capabilities of deep learning (DL) and reinforcement learning (RL), DRL gives rise to a robust and adaptive approach that excels in complex decision-making and control scenarios. With its successful applications in various domains, DRL has been increasingly applied to optimize energy systems, including energy management, demand response, smart grids, and operational control. This paper provides a thorough review of DRL's fundamental principles, models, and algorithm, followed by an in-depth exploration of its applications in energy optimization. Furthermore, the paper discusses recent breakthroughs in DRL, including its integration with traditional methods, and examines the opportunities and challenges of its applications in the energy sectorItem MATHEMATICAL MODELING OF ROAD TRAFFIC FLOW IN URBAN AREAS WITH NEURAL NETWORK(2024) BABALOLA, BABALOLAstudy of traffic flow in urban areas is of paramount importance due to its significant impact on transportation efficiency, environmental sustainability, and overall quality of life. Mathematical modeling serves as a powerful tool to analyze and understand the intricate behaviors exhibited by vehicular traffic within urban settings. This study explores the development and application of mathematical models to characterize the complex interactions among vehicles, pedestrians, infrastructure, and environmental factors in urban traffic systems. The study uses ANN based model for detecting the traffic flow in the urban. And the model is trained on dataset from Kaggle for the ANN algorithms with the mathematical liner equations. Additionally, the study delves into the incorporation of factors such as traffic signals, road geometries, driver psychology, and emerging technologies like connected and autonomous vehicles into these models. Through the synthesis of empirical data, advanced simulation techniques, and theoretical analysis, mathematical models offer valuable insights into traffic management strategies, congestion mitigation, urban planning, and the development of intelligent transportation systems for future citiesItem INTEGRATION OF U-NET AND MASK R-CNN APPROACH FOR AUTOMATED CLASSIFICATION OF HISTOPATHOLOGY IMAGES OF PROSTATE CANCER.(2024) ADEYEMI KHALILULLAHI ADEBAYOThis study proposes a novel approach for automated classification of histopathology images of prostate cancer by integrating U-Net and Mask R-CNN models. The U-Net model is designed to perform segmentation, localizing regions indicative of prostate cancer, while the Mask R-CNN model is optimized for object detection, enhancing classification precision. Our model implementation leverages modified TensorFlow and Mask R-CNN configurations, utilizing custom dataset generation and preprocessing pipelines to handle labeled prostate histopathology images. The dataset is split into training and testing sets, with the U-Net model trained for segmentation tasks, supported by a data generator class for efficient batch processing. After training, the model’s performance is evaluated using metrics such as accuracy, F1 score, recall, and AUC. The initial results demonstrate promising capabilities in accurately segmenting and classifying cancerous regions in histopathology images, indicating the potential for improving diagnostic accuracy in prostate cancer.Item DESIGN AND IMPLEMENTATION OF AN ANDROID-BASED CUMULATIVE GRADE POINT AVERAGE CALCULATOR(2022) ABDULWASIU JAMIU OLASUNKANMIUI/UX refers to the practice of designing digital products with a user-first approach. An android mobile device enables open-source applications embedding UI/UX interface, which enable its high percentage of usage in the student populace. It is being discovered that poor performance of students in assessments can be linked to limited study and concentration which has been traced to the common lack of drive. The GPA calculator App is developed to drive the student to study & prepare hard for the assessment and even predict the grade. This project was developed using java programming language and React JS. The developed GPA Calculator has been hosted on Netlify and published on Google Play Store. The GPA calculator is effective to drive the student to perform well in pursuing good grades.Item CRIME ANALYSIS AND PREDICTION USING DATA MINING TECHNIQUES(2023) IBRAHIM SODIQ OLANIYICRIME ANALYSIS HAS EVOLVED SIGNIFICANTLY WITH THE INTEGRATION OF DATA MINING TECHNIQUES, ENABLING LAW ENFORCEMENT AGENCIES TO UNCOVER HIDDEN PARTTERNS AND PREDICT POTENTIAL CRIMINAL ACTIVITIES. THIS STUDY EXPLORES THE APPLICATION OF DATA MINING ALGORITHMS TO ANALYZE CRIME DATA AND FORECAST FUTURE TRENDS.Item DEVELOPMENT OF FACIAL RECOGNITION-BASED TODDLER’S EMOTION PREDICTION SYSTEM(2023) BASHIRU, BASIT AYOMIDE.Toddler’s emotion is a set of expressions; facial or verbal ranging differently in toddlers, these expressions are usually determined by the environment. However, inability to detect and predict change in toddler’s emotion lead to late intervention in toddler’s development resulting to a negative impact on the mental and social development. To proffer a long-term solution to this problem, the study developed a facial recognition-based toddler’s emotion prediction system. The system’s model is developed using random forest algorithm and trained with the features extracted from the Toddler’s happy and sad facial dataset of 2168 image sample size. Feature extraction of the images is done using Mediapipe machine learning algorithm. The model was integrated into a designed user interface for ease of use. The interface captures the toddler’s face image and makes emotion predictions based on happy or sad. In conclusion, the developed facial recognition-based toddler’s emotion prediction model performs excellently with an accuracy score of 84%. With the help of the recognition rate of the developed system, the study is to predict toddler’s emotion based on two classification which can either be happy or sad at real-time which will aid parent or caregiver on how to treat the toddler based on emotion detected.Item A FACE DETECTION AND RECOGNITION-BASED ONLINE ATTENDANCE SYSTEM USING COMPUTER VISION(2023) NAFISAH, ADEDAYO SULAIMAN.Face recognition has drawn a lot of attention recently and is a crucial issue in many applications, including access control, security systems, and credit card verification and identification of criminals. This study suggests three primary subsystems, including autonomous door access control, face detection, and face recognition. By adapting the principal component analysis (PCA) approach to the fast based principal component analysis (FBPCA) approach, the face identification and detection process is achieved. The captured image is recognized using a web camera and compared with the image in the database. To achieve the goal of identification, image processing and recognition are applied to the actual image modification and transformation. This project focuses on the design and of a facial recognition attendance system using computer vision technique. The system aims to automate the process of identifying and verifying individuals in an organization based on their facial features. By leveraging advanced algorithms, image processing techniques, and deep learning models, the system achieves accurate and real time facial recognition. The project involves data collection, pre-processing, feature extraction, and system integration to develop a comprehensive facial recognition solution