Journal of King Saud University - Computer and Information Sciences

Mensah Kwabena Patrick

Adebayo Felix Adekoya

Ayidzoe Abra Mighty

Baagyire Y. Edward

Capsule Networks – A survey

胶囊网络 – 综述

Recent advances in Intelligent Transport Systems (ITS) and Artificial Intelligence (AI) have stimulated and paved the way toward the widespread introduction of Autonomous Vehicles (AVs). This has opened new opportunities for smart roads, intelligent traffic safety, and traveler comfort. Autonomous Vehicles have become a highly popular research topic in recent years because of their significant capability to reduce road accidents and human injuries. This paper is an attempt to survey all recent AI based techniques used to deal with major functions in AVs, namely scene understanding, motion planning, decision making, vehicle control, social behavior, and communication. Our survey focuses solely on deep learning and reinforcement learning based approaches; it does not include conventional (shallow) shallow based techniques, a subject that has been extensively investigated in the past. Our survey builds a taxonomy of DL and RL algorithms that have been used so far to bring solutions to the four main issues in autonomous driving. Finally, this survey highlights the open challenges and points out possible future research directions.

Badr Ben Elallid

Nabil Benamar

Abdelhakim Senhaji Hafid

Tajjeeddine Rachidi

Nabil Mrani

A Comprehensive Survey on the Application of Deep and Reinforcement Learning Approaches in Autonomous Driving

深度强化学习在自动驾驶中的应用综述

Raza Imam

Kaushal Kumar

Syed Mehran Raza

Rumi Sadaf

Faisal Anwer

Noor Fatima

Mohammad Nadeem

Mohamed Abbas

Obaidur Rahman

A systematic literature review of attribute based encryption in health services

基于属性的加密在健康服务中的系统文献综述

Congestion in public places is one of the major problems in public transportation systems and causes a high level of discomfort for the commuters. Traditionally, overcrowding is detected by manually monitoring and analyzing the video streams from the surveillance cameras, which might lead to errors due to limited human activity. On the other hand, current machine learning models for automatic congestion detection require a massive amount of labeled data to train the network. These models suffer from the over-fitting problem and cannot be generalized to novel scenes. First, we propose a novel synthetic dataset for congestion detection in public places to address these problems. Secondly, we propose a Bidirectional Long-short-term-memory (Bi-LSTM) model that exploits synthetic datasets to boost the performance of congestion detection in the wild. We adopt a domain adaptation strategy to bridge the gap between the real and synthetic data by pre-train the model on the synthetic dataset and then fine-tuning the model on real data. From experiment results, we observe that the proposed framework achieves a significant performance boost on the real datasets after training on the synthetic dataset.

Saleh Basalamah

Sultan Daud Khan

Emad Felemban

Atif Naseer

Faizan Ur Rehman

Deep learning framework for congestion detection at public places via learning from synthetic data

通过合成数据学习实现公共场所拥堵检测的深度学习框架

The problem of detecting key nodes in a network (i.e. nodes with the greatest ability to spread an infection) has been studied extensively in the past. Some approaches to key node detection compute node centrality, but there is no formal proof that central nodes also have the greatest spreading capacity. Other methods use epidemiological models (e.g., the SIR model) to describe the spread of an infection and rely on numerical simulations to find out key nodes; these methods are highly accurate but computationally expensive. To efficiently but accurately detect key nodes, we propose a novel deep learning method called Rank by Graph Convolutional Network, RGCN. Our method constructs a subnetwork around each node to estimate its spreading power; then RGCN applies a graph convolutional network to each subnetwork and the adjacency matrix of the network to learn node embeddings. Finally, a neural network is applied to the node embeddings to detect key nodes. Our RGCN method outperforms state-of-the-art approaches such as RCNN and MRCNN by 11.84% and 13.99%, respectively, when we compare the Kendall's τ coefficient between the node ranking produced by each method with the true ranking obtained by SIR simulations.

Luyuan Gao

Xiaoyang Liu

Chao Liu

Yihao Zhang

Giacomo Fiumara

Pasquale De Meo

Key nodes identification in complex networks based on subnetwork feature extraction

基于子网络特征提取的复杂网络关键节点识别

Alanod Almasaud

Heyam H. Al-Baity

On the robustness of arabic aspect-based sentiment analysis: A comprehensive exploration of transformer-based models

关于阿拉伯语基于方面的情感分析的鲁棒性：基于变压器模型的综合探索

Yujiang Wang

Marshima Mohd Rosli

Norzilah Musa

Lei Wang

Improving clustering-based and adaptive position-aware interpolation oversampling for imbalanced data classification

基于聚类的自适应位置感知插值过采样方法在不平衡数据分类中的改进

Qiang Zhao

Moyan Zhang

Hongjuan Li

Baozhen Song

Yujun Li

Semi-supervised learning for skeleton behavior recognition: A multi-dimensional graph comparison approach

骨架行为识别的半监督学习：一种多维图比较方法

Adel R. Alharbi

Amer Aljaedi

Abdullah Aljuhni

Moahd K. Alghuson

Hussain Aldawood

Sajjad Shaukat Jamal

Tariq Shah

Enhancing Internet of Things communications: Development of a new S-box and multi-layer encryption framework

增强物联网通信：新型S盒与多层加密框架的开发

Baha Ihnaini

Belal Abuhaija

Ebenezer Atta Mills

Massudi Mahmuddin

Semantic similarity on multimodal data: A comprehensive survey with applications

多模态数据语义相似性：综合调查与应用

Abbreviation	J. King Saud Univ. Comput. Inf. Sci.
Journal Impact	5.38
Quartiles(Global)	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q1)

ISSN	1319-1578, 2213-1248
h-index	60

Publication Information	Publisher: King Saud bin Abdulaziz University，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2014，Proportion of original research papers: 95.23%，Self Citation Rate:5.80%， Gold OA Rate: 94.61%
Journal Fees	$1350Details
Average review cycle	网友分享经验：45 Weeks

Journal of King Saud University - Computer and Information Sciences

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