scholarly journals An Outlook on the Interplay of Machine Learning and Reconfigurable Intelligent Surfaces: An Overview of Opportunities and Limitations

2021 ◽  
Author(s):  
Lina Mohjazi ◽  
Ahmed Zoha ◽  
Lina Bariah ◽  
sami muhaidat ◽  
Paschalis C. Sofotasios ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Wireless Communication ◽  
Paradigm Shift ◽  
Electromagnetic Waves ◽  
State Of The Art ◽  
Look Ahead ◽  
Art Research ◽  
New Applications ◽  
Storage Energy

<div>Recent advances in programmable metasurfaces, also dubbed as reconfigurable intelligent surfaces (RISs), are</div><div>envisioned to offer a paradigm shift from uncontrollable to fully tunable and customizable wireless propagation environments, enabling a plethora of new applications and technological trends. Therefore, in view of this cutting edge technological concept, we first review the architecture and electromagnetic waves manipulation functionalities of RISs. We then detail some of the recent advancements that have been made towards realizing these programmable functionalities in wireless communication applications. Furthermore, we elaborate on how machine learning (ML) can address various constraints introduced by real-time deployment of RISs, particularly in terms of latency, storage, energy efficiency, and computation. A review of the state-of-the-art research on the integration of ML with RISs is presented, highlighting their potentials as well as challenges. Finally, the paper concludes by offering a look ahead towards unexplored possibilities of ML mechanisms in the context of RISs. </div>

scholarly journals An Outlook on the Interplay of Machine Learning and Reconfigurable Intelligent Surfaces: An Overview of Opportunities and Limitations

2021 ◽  
Author(s):  
Lina Mohjazi ◽  
Ahmed Zoha ◽  
Lina Bariah ◽  
sami muhaidat ◽  
Paschalis C. Sofotasios ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Wireless Communication ◽  
Paradigm Shift ◽  
Electromagnetic Waves ◽  
State Of The Art ◽  
Look Ahead ◽  
Art Research ◽  
New Applications ◽  
Storage Energy

<div>Recent advances in programmable metasurfaces, also dubbed as reconfigurable intelligent surfaces (RISs), are</div><div>envisioned to offer a paradigm shift from uncontrollable to fully tunable and customizable wireless propagation environments, enabling a plethora of new applications and technological trends. Therefore, in view of this cutting edge technological concept, we first review the architecture and electromagnetic waves manipulation functionalities of RISs. We then detail some of the recent advancements that have been made towards realizing these programmable functionalities in wireless communication applications. Furthermore, we elaborate on how machine learning (ML) can address various constraints introduced by real-time deployment of RISs, particularly in terms of latency, storage, energy efficiency, and computation. A review of the state-of-the-art research on the integration of ML with RISs is presented, highlighting their potentials as well as challenges. Finally, the paper concludes by offering a look ahead towards unexplored possibilities of ML mechanisms in the context of RISs. </div>

scholarly journals Spam Review Detection:A Systematic Literature Review

2020 ◽  
Author(s):  
Muhammad Shoaib Farooq
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
State Of The Art ◽  
The State ◽  
Research Gaps ◽  
Art Research ◽  
Fake Reviews ◽  
Research Studies

In this era of technology, people rely on online posted reviews before buying any product. These reviews are very important for both the consumers and people. Consumers and people use this information for decision making while buying products or investing money in any product. This has inclined the spammers to generate spam or fake reviews so that they can recommend their products and beat the competitors. Spammers have developed many systems to generate the bulk of spam reviews within hours. Many techniques, strategies have been designed and recommended to resolve the issue of spam reviews. In this paper, a complete review of existing techniques and strategies for detecting spam review is discussed. Apart from reviewing the state-of-the-art research studies on spam review detection, a taxonomy on techniques of machine learning for spam review detection has been proposed. Moreover, its focus on research gaps and future recommendations for spam review identification.

scholarly journals Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping

2016 ◽  
Author(s):  
Michael P. Pound ◽  
Alexandra J. Burgess ◽  
Michael H. Wilson ◽  
Jonathan A. Atkinson ◽  
Marcus Griffiths ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Analysis ◽  
Paradigm Shift ◽  
State Of The Art ◽  
Plant Phenotyping ◽  
Learning Approaches ◽  
Challenging Problem ◽  
Feature Identification ◽  
Art Performance

AbstractDeep learning is an emerging field that promises unparalleled results on many data analysis problems. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-the-art results for root and shoot feature identification and localisation. We predict a paradigm shift in image-based phenotyping thanks to deep learning approaches.

Applications of Machine learning in Computational Nanotechnology

2021 ◽  
Author(s):  
Wenxiang Liu ◽  
Yongqiang Wu ◽  
Yang Hong ◽  
Zhongtao Zhang ◽  
Yanan Yue ◽  
...  
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
State Of The Art ◽  
Material Design ◽  
Research Progress ◽  
Time Investment ◽  
Research Activities ◽  
Art Research ◽  
Applications Of Machine Learning ◽  
Computational Nanotechnology

Abstract Machine learning (ML) has gained extensive attentions in recent years due to its powerful data analysis capabilities. It has been successfully applied to many fields and helped the researchers to achieve several major theoretical and applied breakthroughs. Some of the notable applications in the field of computational nanotechnology are machine learning potentials, property prediction and material discovery. This review summarizes of the state-of-the-art research progress in these three fields. Machine learning potentials bridge the efficiency vs. accuracy gap between density functional calculations (DFT) and classical molecular dynamics (MD). For property predictions, machine learning provides a robust method that eliminate the needs of repetitive calculations for different simulation setup. Material design and drug discovery assisted by machine learning greatly reduces the capital and time investment by orders of magnitude. In this perspective, several common machine learning potentials and machine learning models are firstly introduced. Using these state-of-the-art models, developments in property predictions and material discovery are overviewed, respectively. Finally, this paper was concluded with an outlook on future directions of data-driven research activities in computational nanotechnology.

A Review of Deep Machine Learning

2016 ◽  
Vol 24 ◽  
pp. 124-136 ◽  
Author(s):  
Ben Bright Benuwa ◽  
Yong Zhao Zhan ◽  
Benjamin Ghansah ◽  
Dickson Keddy Wornyo ◽  
Frank Banaseka Kataka
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Paradigm Shift ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Algorithm Design ◽  
Prior State ◽  
Art Techniques

The rapid increase of information and accessibility in recent years has activated a paradigm shift in algorithm design for artificial intelligence. Recently, deep learning (a surrogate of Machine Learning) have won several contests in pattern recognition and machine learning. This review comprehensively summarises relevant studies, much of it from prior state-of-the-art techniques. This paper also discusses the motivations and principles regarding learning algorithms for deep architectures.

scholarly journals Machine Learning in Beyond 5G/6G Networks—State-of-the-Art and Future Trends

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2786
Author(s):  
Vasileios P. Rekkas ◽  
Sotirios Sotiroudis ◽  
Panagiotis Sarigiannidis ◽  
Shaohua Wan ◽  
George K. Karagiannidis ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Wireless Communications ◽  
Wireless Communication ◽  
Communication Systems ◽  
State Of The Art ◽  
Wireless Communication Systems ◽  
Future Trends ◽  
Network Applications ◽  
Open Issues

Artificial Intelligence (AI) and especially Machine Learning (ML) can play a very important role in realizing and optimizing 6G network applications. In this paper, we present a brief summary of ML methods, as well as an up-to-date review of ML approaches in 6G wireless communication systems. These methods include supervised, unsupervised and reinforcement techniques. Additionally, we discuss open issues in the field of ML for 6G networks and wireless communications in general, as well as some potential future trends to motivate further research into this area.

scholarly journals Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods

2021 ◽  
Vol 71 ◽  
pp. 1183-1317
Author(s):  
Aditya Mogadala ◽  
Marimuthu Kalimuthu ◽  
Dietrich Klakow
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
State Of The Art ◽  
Problem Formulation ◽  
Future Directions ◽  
Language Research ◽  
Language And Vision ◽  
New Applications ◽  
Vision And Language ◽  
Made In

Interest in Artificial Intelligence (AI) and its applications has seen unprecedented growth in the last few years. This success can be partly attributed to the advancements made in the sub-fields of AI such as machine learning, computer vision, and natural language processing. Much of the growth in these fields has been made possible with deep learning, a sub-area of machine learning that uses artificial neural networks. This has created significant interest in the integration of vision and language. In this survey, we focus on ten prominent tasks that integrate language and vision by discussing their problem formulation, methods, existing datasets, evaluation measures, and compare the results obtained with corresponding state-of-the-art methods. Our efforts go beyond earlier surveys which are either task-specific or concentrate only on one type of visual content, i.e., image or video. Furthermore, we also provide some potential future directions in this field of research with an anticipation that this survey stimulates innovative thoughts and ideas to address the existing challenges and build new applications.

scholarly journals The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review

2020 ◽  
Vol 7 ◽  
Author(s):  
Karthik Seetharam ◽  
Daniel Brito ◽  
Peter D. Farjo ◽  
Partho P. Sengupta
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Paradigm Shift ◽  
State Of The Art ◽  
Cardiovascular Imaging ◽  
Review Article ◽  
Imaging Modalities ◽  
Data Explosion ◽  
Digital Landscape

In this current digital landscape, artificial intelligence (AI) has established itself as a powerful tool in the commercial industry and is an evolving technology in healthcare. Cutting-edge imaging modalities outputting multi-dimensional data are becoming increasingly complex. In this era of data explosion, the field of cardiovascular imaging is undergoing a paradigm shift toward machine learning (ML) driven platforms. These diverse algorithms can seamlessly analyze information and automate a range of tasks. In this review article, we explore the role of ML in the field of cardiovascular imaging.

scholarly journals Diagnostic Impact of Radiological Findings and Extracellular Vesicles: Are We Close to Radiovesicolomics?

Biology ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1265
Author(s):  
Francesco Lorenzo Serafini ◽  
Paola Lanuti ◽  
Andrea Delli Pizzi ◽  
Luca Procaccini ◽  
Michela Villani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Extracellular Vesicles ◽  
Laboratory Tests ◽  
State Of The Art ◽  
The Other ◽  
Radiological Findings ◽  
Radiological Features ◽  
Precise Diagnosis ◽  
Art Research

Currently, several pathologies have corresponding and specific diagnostic and therapeutic branches of interest focused on early and correct detection, as well as the best therapeutic approach. Radiology never ceases to develop newer technologies in order to give patients a clear, safe, early, and precise diagnosis; furthermore, in the last few years diagnostic imaging panoramas have been extended to the field of artificial intelligence (AI) and machine learning. On the other hand, clinical and laboratory tests, like flow cytometry and the techniques found in the “omics” sciences, aim to detect microscopic elements, like extracellular vesicles, with the highest specificity and sensibility for disease detection. If these scientific branches started to cooperate, playing a conjugated role in pathology diagnosis, what could be the results? Our review seeks to give a quick overview of recent state of the art research which investigates correlations between extracellular vesicles and the known radiological features useful for diagnosis.

scholarly journals Spam Review Detection:A Systematic Literature Review

2020 ◽  
Author(s):  
Muhammad Shoaib Farooq
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
State Of The Art ◽  
The State ◽  
Research Gaps ◽  
Art Research ◽  
Fake Reviews ◽  
Research Studies

In this era of technology, people rely on online posted reviews before buying any product. These reviews are very important for both the consumers and people. Consumers and people use this information for decision making while buying products or investing money in any product. This has inclined the spammers to generate spam or fake reviews so that they can recommend their products and beat the competitors. Spammers have developed many systems to generate the bulk of spam reviews within hours. Many techniques, strategies have been designed and recommended to resolve the issue of spam reviews. In this paper, a complete review of existing techniques and strategies for detecting spam review is discussed. Apart from reviewing the state-of-the-art research studies on spam review detection, a taxonomy on techniques of machine learning for spam review detection has been proposed. Moreover, its focus on research gaps and future recommendations for spam review identification.

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