Workload scheduling in cloud: A comprehensive survey and future research directions

2017 ◽  
Author(s):  
S R Shishira ◽  
A. Kandasamy ◽  
K. Chandrasekaran
Keyword(s):  
Future Research ◽  
Research Directions ◽  
Workload Scheduling ◽  
Comprehensive Survey ◽  
Future Research Directions

MetaLearning with Graph Neural Networks

2021 ◽  
Vol 23 (2) ◽  
pp. 13-22
Author(s):  
Debmalya Mandal ◽  
Sourav Medya ◽  
Brian Uzzi ◽  
Charu Aggarwal
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Graph Problems ◽  
Meta Learning ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Graph Neural Networks

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different metalearning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.

scholarly journals Carpooling in Connected and Autonomous Vehicles: Current Solutions and Future Directions

2022 ◽  
Author(s):  
Farkhanda Zafar ◽  
Hasan Ali Khattak ◽  
Moayad Aloqaily ◽  
Rasheed Hussain
Keyword(s):  
Business Model ◽  
Autonomous Vehicles ◽  
Research Work ◽  
Relevant Information ◽  
Security And Privacy ◽  
Future Research ◽  
Research Directions ◽  
Comprehensive Survey ◽  
One Stop ◽  
Future Research Directions

Owing to the advancements in communication and computation technologies, the dream of commercialized connected and autonomous cars is becoming a reality. However, among other challenges such as environmental pollution, cost, maintenance, security, and privacy, the ownership of vehicles (especially for Autonomous Vehicles (AV)) is the major obstacle in the realization of this technology at the commercial level. Furthermore, the business model of pay-as-you-go type services further attracts the consumer because there is no need for upfront investment. In this vein, the idea of car-sharing ( aka carpooling) is getting ground due to, at least in part, its simplicity, cost-effectiveness, and affordable choice of transportation. Carpooling systems are still in their infancy and face challenges such as scheduling, matching passengers interests, business model, security, privacy, and communication. To date, a plethora of research work has already been done covering different aspects of carpooling services (ranging from applications to communication and technologies); however, there is still a lack of a holistic, comprehensive survey that can be a one-stop-shop for the researchers in this area to, i) find all the relevant information, and ii) identify the future research directions. To fill these research challenges, this paper provides a comprehensive survey on carpooling in autonomous and connected vehicles and covers architecture, components, and solutions, including scheduling, matching, mobility, pricing models of carpooling. We also discuss the current challenges in carpooling and identify future research directions. This survey is aimed to spur further discussion among the research community for the effective realization of carpooling.

State of the Art and Key Design Challenges of Telesurgical Robotics

2019 ◽  
pp. 1100-1111
Author(s):  
Sajid Nisar ◽  
Osman Hasan
Keyword(s):  
State Of The Art ◽  
Robotic Systems ◽  
Future Research ◽  
Research Directions ◽  
Remote Locations ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Surgical System ◽  
Surgical Operations ◽  
Design Challenges

Telesurgical robotic systems allow surgeons to perform surgical operations from remote locations with enhanced comfort and dexterity. Introduction of robotic technology has revolutionized operation theaters but its multidisciplinary nature and high associated costs pose significant challenges. This chapter provides a comprehensive survey of the current progress in the field of surgical robotics with a detailed discussion on various state-of-the-art telesurgical robotic systems. The key design approaches and challenges are identified, and their solutions are recommended. A set of parameters that can be used to assess usefulness of a telesurgical robot are discussed. Finally, guidelines for selection of a suitable surgical system and the future research directions are described.

scholarly journals You can Try without Visiting: A Comprehensive Survey on Virtually Try-on Outfits

2021 ◽  
Author(s):  
Hajer Ghodhbani ◽  
Adel Alimi ◽  
Mohamed Neji ◽  
Imran Razzak
Keyword(s):  
Deep Learning ◽  
Literature Review ◽  
Research Field ◽  
Future Research ◽  
Fashion Industry ◽  
Research Directions ◽  
Comprehensive Literature Review ◽  
Benchmark Datasets ◽  
Comprehensive Survey ◽  
Future Research Directions

<p>Our work aims to conduct a comprehensive literature review of deep learning methods applied in the fashion industry and, especially, the image-based virtual fitting task by citing research works published in the last years. We have summarized their challenges, their main frameworks, the popular benchmark datasets, and the different evaluation metrics. Also, some promising future research directions are discussed to propose improvements in this research field.</p>

State of the Art and Key Design Challenges of Telesurgical Robotics

2018 ◽  
pp. 6872-6881 ◽  
Author(s):  
Sajid Nisar ◽  
Osman Hasan
Keyword(s):  
State Of The Art ◽  
Robotic Systems ◽  
Future Research ◽  
Research Directions ◽  
Remote Locations ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Surgical System ◽  
Surgical Operations ◽  
Design Challenges

Telesurgical robotic systems allow surgeons to perform surgical operations from remote locations with enhanced comfort and dexterity. Introduction of robotic technology has revolutionized operation theaters but it's multidisciplinary nature and high associated costs pose significant challenges. This chapter provides a comprehensive survey of the current progress in the field of surgical robotics with a detailed discussion on various state-of-the-art telesurgical robotic systems. The key design approaches and challenges are identified and their solutions are recommended. A set of parameters that can be used to assess usefulness of a telesurgical robot are discussed. Finally, guidelines for selection of a suitable surgical system and the future research directions are described.

scholarly journals Android Malware Family Classification and Analysis: Current Status and Future Directions

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 942 ◽  
Author(s):  
Fahad Alswaina ◽  
Khaled Elleithy
Keyword(s):  
Three Dimensions ◽  
Current Status ◽  
Future Research ◽  
Future Directions ◽  
Research Directions ◽  
Android Malware ◽  
Literature Reviews ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Major Attention

Android receives major attention from security practitioners and researchers due to the influx number of malicious applications. For the past twelve years, Android malicious applications have been grouped into families. In the research community, detecting new malware families is a challenge. As we investigate, most of the literature reviews focus on surveying malware detection. Characterizing the malware families can improve the detection process and understand the malware patterns. For this reason, we conduct a comprehensive survey on the state-of-the-art Android malware familial detection, identification, and categorization techniques. We categorize the literature based on three dimensions: type of analysis, features, and methodologies and techniques. Furthermore, we report the datasets that are commonly used. Finally, we highlight the limitations that we identify in the literature, challenges, and future research directions regarding the Android malware family.

scholarly journals A Comprehensive Survey of COVID-19 Detection Using Medical Images

2020 ◽  
Author(s):  
Faisal Muhammad shah ◽  
Sajib Kumar Saha Joy ◽  
Farzad Ahmed ◽  
Mayeesha Humaira ◽  
Amit Saha Ami ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Images ◽  
Future Research ◽  
Research Directions ◽  
X Ray ◽  
Time Period ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
The Cost ◽  
Day By Day

The outbreak of the COVID-19 pandemic caused the death of a large number of people. Millions ofpeople are infected by this virus and are still getting infected day by day. As the cost and required time ofconventional RT-PCR tests to detect COVID-19, researchers are trying to use medical images like X-Ray andComputed Tomography (CT) images to detect it with the help of Artificial Intelligence (AI) based systems. Inthis paper, we reviewed some of these newly emerging AI-based models that can detect COVID-19 frommedical images using X-Ray or CT of lung images. We collected information about available research resourcesand inspected a total of 80 papers from the time period of February 21, 2020 to June 20, 2020. We explored andanalyzed datasets, preprocessing techniques, segmentation, feature extraction, classification and experimentalresults which can be helpful for finding future research directions in the domain of automatic diagnosis ofCovid-19 disease using Artificial Intelligence (AI) based frameworks.

A SURVEY OF PRIVACY-PRESERVING COLLABORATIVE FILTERING SCHEMES

2013 ◽  
Vol 23 (08) ◽  
pp. 1085-1108 ◽  
Author(s):  
ALPER BILGE ◽  
CIHAN KALELI ◽  
IBRAHIM YAKUT ◽  
IHSAN GUNES ◽  
HUSEYIN POLAT
Keyword(s):  
Collaborative Filtering ◽  
Privacy Preserving ◽  
Data Partitioning ◽  
Future Research ◽  
Practical Implementation ◽  
Research Directions ◽  
Implementation Challenges ◽  
Confidential Data ◽  
Comprehensive Survey ◽  
Future Research Directions

With increasing need for preserving confidential data while providing recommendations, privacy-preserving collaborative filtering has been receiving increasing attention. To make data owners feel more comfortable while providing predictions, various schemes have been proposed to estimate recommendations without deeply jeopardizing privacy. Such methods eliminate or reduce data owners' privacy, financial, and legal concerns by employing different privacy-preserving techniques. Although there are considerable numbers of studies focusing on privacy-preserving collaborative filtering schemes, there is no comprehensive survey investigating them with respect to different directions. In this survey, we mainly focus on studying various privacy-preserving recommendation methods according to the data partitioning cases and the utilized techniques for preserving confidentiality. We also review privacy in general and examine in collaborative filtering scenarios. We discuss the proposed schemes in terms of their limitations and practical implementation challenges. Moreover, we give an overview of evaluation of such schemes. We finally provide a comprehensive guideline for studying in this area and propose future research directions.

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