A5015515752- Anomaly Detection Techniques and Applications
- Privacy-Preserving Technologies in Data
- Data Management and Algorithms
- Machine Learning in Healthcare
- COVID-19 diagnosis using AI
- Stock Market Forecasting Methods
- Bayesian Modeling and Causal Inference
- Advanced Image Fusion Techniques
- Energy Load and Power Forecasting
- Energy Efficient Wireless Sensor Networks
- Imbalanced Data Classification Techniques
- Smart Parking Systems Research
- Infrared Target Detection Methodologies
- Data Mining Algorithms and Applications
- Satellite Image Processing and Photogrammetry
- Statistical Methods and Bayesian Inference
- IoT-based Smart Home Systems
- Market Dynamics and Volatility
- Cryptography and Data Security
- Data Stream Mining Techniques
- Smart Agriculture and AI
- Time Series Analysis and Forecasting
- Context-Aware Activity Recognition Systems
- Human Pose and Action Recognition
- Blockchain Technology Applications and Security
King Khalid University
2019-2024
University of East Anglia
2018-2022
Human Action Recognition (HAR) is a vital area of computer vision with diverse applications in security, healthcare, and human-computer interaction. Addressing the challenges HAR, particularly dynamic complex environments, essential to advancing this field. The Actions Diverse Environments (HADE) framework introduced paper represents significant advancement improving capabilities Convolutional Neural Networks (CNNs) for effective HAR. strength HADE its carefully curated dataset, primarily...
Abstract Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose approach to missing classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble compares types of ensembles: bagging stacking. We also experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, introduce...
The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic tools. In this article, a hybrid approach in terms datasets as well methodology by utilizing previously unexplored dataset obtained from private hospital detecting COVID-19, pneumonia, normal conditions chest X-ray images (CXIs) is proposed coupled with Explainable Artificial Intelligence (XAI). Our study leverages less preprocessing pre-trained cutting-edge models like InceptionV3, VGG16, VGG19...
In recent years, disease attacks have posed continuous threats to agriculture and caused substantial losses in the economy. Thus, early detection classification could minimize spread of help improve yield. Meanwhile, deep learning has emerged as significant approach detecting classifying images. The performed using mainly relies on large datasets prevent overfitting problems. Automatic Segmentation Hyper Parameter Optimization Artificial Rabbits Algorithm (AS-HPOARA) is developed overcome...
Time series classification has become an interesting field of research, thanks to the extensive studies conducted in past two decades. may have missing data, which affect both representation and also modeling time series. Thus, recovering data using appropriate series-based imputation methods is essential step. Multiple a recovery method where it produced multiple imputed data. The proves its usefulness terms reflecting uncertainty inherit data; however, under-researched problems. In this...
The objective of this paper is the use new hybrid meta-heuristic method called Guided Best-So-Far Honey Bees Inspired Algorithm with Artificial Neural Network (ANN) on Prediction Crude Oil Prices Kingdom Saudi Arabia (KSA). Very high volatility crude oil prices one main hurdles for economic development; therefore, it’s need hour to predict prices, especially oil-rich countries such as KSA. Hence, in paper, we are proposing a algorithm, named: Bee Colony (GBABC) algorithm. proposed algorithm...
Abstract Internet of Things (IoT) has rapidly expanded with the interconnection various devices through wireless networks. However, this widespread deployment IoT posed challenges in managing access to device resources due their vast quantity and scale. As these generate share sensitive data, ensuring secure becomes paramount. Traditional control systems like Discretionary Access Control (DAC), Intelligent Dynamic Bandwidth (IBAC), Mandatory (MAC) have limitations such as centralization,...