Abdullah Al-Dhelaan

TL;DR: This paper revise the user-based collaborative filtering (CF) technique, and proposes two recommendation approaches fusing usergenerated tags and social relations in a novel way that achieve more precise recommendations than the compared approaches.

TL;DR: The traditional locality sensitive hashing method is improved to implement fast query of nearest neighbors and several definitions are redefined on the basis of the influence space of each object, which takes the nearest neighbor and the reverse nearest neighbors into account.

TL;DR: A new hybrid method has been proposed for image clustering based on combining the particle swarm optimization (PSO) with k-means clustering algorithms that uses the color and texture images as visual features to represent the images.

TL;DR: This paper proposes a centralized algorithm Non-Linear Approximation Optimization for Proportional Fairness (NLAO-PF) to derive the user-AP association via relaxation and proposes a distributed heuristic Best Performance First (BPF) based on a novel performance revenue function, which provides an AP selection criterion for newcomers.

TL;DR: A low intricacy technique for contrast enhancement is proposed, and its performance is exhibited against various versions of histogram-based enhancement technique using three advanced image quality assessment metrics of Universal Image Quality Index (UIQI), Structural Similarity Index (SSIM), and Feature Similarity index (FSIM).