Showing papers in "Journal of King Saud University - Computer and Information Sciences in 2021"

TL;DR: A critical comparison of different algorithm proposed by researchers for quality inspection of fruits and vegetables has been carried out and a detailed overview of various methods i.e. preprocessing, segmentation, feature extraction, classification which addressed fruit and vegetables quality based on color, texture, size, shape and defects is presented.

TL;DR: A survey of Face Expression Recognition techniques which include the three major stages such as preprocessing, feature extraction and classification, and explains the various types of FER techniques with its major contributions.

TL;DR: Insight is gained to various open issues and research directions to explore the promising areas for future developments in data compression techniques and its applications.

TL;DR: This study presents and classifies the work done on the three varieties of Arabic, by concentrating on both Arabic and Arabizi, and associates each work to its publicly available resources whenever available.

TL;DR: A comprehensive overview of fault tolerance-related issues in cloud computing is presented, emphasizing upon the significant concepts, architectural details, and the state-of-art techniques and methods.

TL;DR: A clustering based approach to distinguish the data representing flows of network traffic which include both normal and Distributed Denial of Service (DDoS) traffic is provided.

TL;DR: A novel idea for choosing the initial centroids in K- Means using traditional K-means and Fuzzy C- Means algorithms is proposed and the results obtained are compared with one another by their iterations, cluster compactness and execution time.

TL;DR: The paper proposed the prediction model of Apple disease in the apple orchards of Kashmir valley using data analytics and Machine learning in IoT system and discusses the challenges faced when incorporating these technologies in the traditional farming approaches.

TL;DR: A novel multilevel thresholding algorithm using a meta-heuristic Krill Herd Optimization (KHO) algorithm has been proposed for solving the image segmentation problem.

TL;DR: A new mechanism for route selection combining Ad-hoc On-Demand Distance Vector (AODV) protocol with Ant Colony Optimization (ACO) protocol to improve Quality of Service (QoS) in MANET is proposed.

TL;DR: The experimental result shows that the proposed Android based course attendance system achieved face recognition accuracy of 97.29 by using linear discriminant analysis and only needed 0.000096s to recognize a face image in the server.

TL;DR: The main focus of the paper is on resource management in pervasive IoT environment with limited resources, and a use case of IoT based Body Area Network is presented and a model for resourcemanagement in personal and community healthcare is proposed.

TL;DR: This paper presents a comprehensive review of various Load Balancing techniques in a static, dynamic, and nature-inspired cloud environment to address the Data Center Response Time and overall performance.

TL;DR: A help desk system that acts as a single point of contact between users and IT staff is introduced and utilizes an accurate ticket classification machine learning model to associate a help desk ticket with its correct service from the start and hence minimize ticket resolution time, save human resources, and enhance user satisfaction.

TL;DR: Experimental results prove the proposed SRAFBN network model not only has higher PSNR and SSIM, but also can reconstruct more realistic and clear real-time images.

TL;DR: Python-based recreation outputs show that ESO-LEACH outflanks conventional LEACH, and enhances the network’s life span, and indicates that the enhanced proposed algorithm is successful in extending network lifespan adequately.

TL;DR: This work identifies the infected fishes caused by the various pathogen in aquaculture using the alliance of flawless image processing and machine learning mechanism with the help of the Support Vector Machine (SVM) algorithm of machine learning with a kernel function.

TL;DR: A hybridised harmony search and Pareto optimization approach for feature selection in high dimensional data classification problem and result reveals that the proposed hybridisation is providing high potentiality in both sample classification and feature subset prediction prospective for high dimensional databases.

TL;DR: A novel architecture for Brain tumor classification and tumor type object detection using the RCNN technique is proposed which has been analyzed using two publicly available datasets from Figshare and Kaggle.

TL;DR: A survey that focuses on the identification of the most relevant health Internet of things (H-IoT) applications supported by smart city infrastructure and evaluates related technologies and systems for RHM services by understanding the most pertinent monitoring applications based on several models with different corresponding IoT-based sensors.

TL;DR: The proposed extractive method evaluates each sentence based on a combination of statistical and semantic features in which a novel formulation is used taking into account sentence importance, coverage and diversity.

TL;DR: This paper presents a comprehensive review on localization system, problems, principle and approaches for mobile robots, and classify the localization problems in to three categories based on the information of initial position of the robot.

TL;DR: This paper aims to evaluate popular consumer-grade EEG devices’ status and review relevant studies that examined the reliability of these low-cost devices for emotion recognition over the last five years, including a comparison with research-grade devices.

TL;DR: A systematically reviews is done on different types of particle swarm optimization (PSO) based scheduling strategy with set of challenges and future direction.

TL;DR: An efficient hybrid mobile cloud computing model based on cloudlets concept is proposed and applied to health care systems as a case study and according to the experimental power and delay results, the hybrid cloud model performs up to 75% better when compared to the traditional cloud models.

TL;DR: This paper helps in identifying suitable combination of Deep learning, Natural language processing and medical imaging to enhance diagnosis and highlighted the major challenges in deploying deep learning in medical imaging and medical natural language processing.

TL;DR: The significance of integrating Blockchain technology in the IoT environment to ensure trust among IoT devices is illustrated and a comparative analysis between traditional and Blockchain-based trust management techniques is presented to represent the significance of Blockchain in ensuring trust.

TL;DR: The proposed method is named as ERMOPI (Emotion Recognition using Meta-learning across Occlusion, Pose and Illumination) which performs emotion recognition from facial expressions using meta-learning approach for still images and it is robust to partial occlusions, varying head poses and illumination levels which is the novelty of this work.

TL;DR: The results are encouraging and the proposed MSDS-MI system is found to be more efficient than Connected Dominating sets, Pseudo Dominating Sets, Dynamic Cluster Head Genetic Algorithm, and Distributed Self-Healing Approach.

TL;DR: A comprehensive study of representative works on IoT network management, analyzes existing solutions for IoT low power networks management and presents a taxonomy of those solutions and compares existing research proposals based on different requirements.