The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

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Artificial intelligence and machine learning in design of mechanical materials

Materials Horizons ◽

10.1039/d0mh01451f ◽

2021 ◽

Author(s):

Kai Guo ◽

Zhenze Yang ◽

Chi-Hua Yu ◽

Markus J. Buehler

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

State Of The Art ◽

The State

This review revisits the state of the art of research efforts on the design of mechanical materials using machine learning.

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NIMG-46. SYSTEMATIC LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE ALGORITHMS USING PRE-THERAPY MR IMAGING FOR GLIOMA MOLECULAR SUBTYPE CLASSIFICATION

Neuro-Oncology ◽

10.1093/neuonc/noab196.545 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi139-vi139

Author(s):

Jan Lost ◽

Tej Verma ◽

Niklas Tillmanns ◽

W R Brim ◽

Harry Subramanian ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Literature Review ◽

Prediction Accuracy ◽

Molecular Subtypes ◽

Molecular Subtype ◽

Tumor Resection ◽

Support Vector ◽

Hospital Data

Abstract PURPOSE Identifying molecular subtypes in gliomas has prognostic and therapeutic value, traditionally after invasive neurosurgical tumor resection or biopsy. Recent advances using artificial intelligence (AI) show promise in using pre-therapy imaging for predicting molecular subtype. We performed a systematic review of recent literature on AI methods used to predict molecular subtypes of gliomas. METHODS Literature review conforming to PRSIMA guidelines was performed for publications prior to February 2021 using 4 databases: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science core-collection. Keywords included: artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Non-machine learning and non-human studies were excluded. Screening was performed using Covidence software. Bias analysis was done using TRIPOD guidelines. RESULTS 11,727 abstracts were retrieved. After applying initial screening exclusion criteria, 1,135 full text reviews were performed, with 82 papers remaining for data extraction. 57% used retrospective single center hospital data, 31.6% used TCIA and BRATS, and 11.4% analyzed multicenter hospital data. An average of 146 patients (range 34-462 patients) were included. Algorithms predicting IDH status comprised 51.8% of studies, MGMT 18.1%, and 1p19q 6.0%. Machine learning methods were used in 71.4%, deep learning in 27.4%, and 1.2% directly compared both methods. The most common algorithm for machine learning were support vector machine (43.3%), and for deep learning convolutional neural network (68.4%). Mean prediction accuracy was 76.6%. CONCLUSION Machine learning is the predominant method for image-based prediction of glioma molecular subtypes. Major limitations include limited datasets (60.2% with under 150 patients) and thus limited generalizability of findings. We recommend using larger annotated datasets for AI network training and testing in order to create more robust AI algorithms, which will provide better prediction accuracy to real world clinical datasets and provide tools that can be translated to clinical practice.

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Debunking the myth of industry 4.0 in health care: insights from a systematic literature review

The TQM Journal ◽

10.1108/tqm-10-2019-0245 ◽

2020 ◽

Vol 32 (4) ◽

pp. 849-868 ◽

Cited By ~ 1

Author(s):

Mauro Cavallone ◽

Rocco Palumbo

Keyword(s):

Artificial Intelligence ◽

Health Care ◽

Health Services ◽

Literature Review ◽

Systematic Literature Review ◽

Industry 4.0 ◽

Services Quality ◽

State Of The Art ◽

The State ◽

Content Type

PurposeIndustry 4.0, artificial intelligence and digitalization have got a momentum in health care. However, scholars and practitioners do not agree on their implications on health services' quality and effectiveness. The article aims at shedding light on the applications, aftermaths and drawbacks of industry 4.0 in health care, summarizing the state of the art.Design/methodology/approachA systematic literature review was undertaken. We arranged an ad hoc research design, which was tailored to the study purposes. Three citation databases were queried. We collected 1,194 scientific papers which were carefully considered for inclusion in this systematic literature review. After three rounds of analysis, 40 papers were taken into consideration.FindingsIndustry 4.0, artificial intelligence and digitalization are revolutionizing the design and the delivery of care. They are expected to enhance health services' quality and effectiveness, paving the way for more direct patient–provider relationships. In addition, they have been argued to allow a more appropriate use of available resources. There is a dark side of health care 4.0 involving both management and ethical issues.Research limitations/implicationsIndustry 4.0 in health care should not be conceived as a self-nourishing innovation; rather, it needs to be carefully steered at both the policy and management levels. On the one hand, comprehensive governance models are required to realize the full potential of health 4.0. On the other hand, the drawbacks of industry 4.0 should be timely recognized and thoroughly addressed.Originality/valueThe article contextualizes the state of the art of industry 4.0 in the health care context, providing some insights for further conceptual and empirical developments.

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Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks

CrossRef Listing of Deleted DOIs ◽

10.1177/1087057116631284 ◽

2016 ◽

Vol 21 (9) ◽

pp. 998-1003 ◽

Cited By ~ 42

Author(s):

Oliver Dürr ◽

Beate Sick

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Single Cell ◽

Convolutional Neural Networks ◽

State Of The Art ◽

Misclassification Rate ◽

Support Vector ◽

Learning Methods ◽

Phenotype Classification

Deep learning methods are currently outperforming traditional state-of-the-art computer vision algorithms in diverse applications and recently even surpassed human performance in object recognition. Here we demonstrate the potential of deep learning methods to high-content screening–based phenotype classification. We trained a deep learning classifier in the form of convolutional neural networks with approximately 40,000 publicly available single-cell images from samples treated with compounds from four classes known to lead to different phenotypes. The input data consisted of multichannel images. The construction of appropriate feature definitions was part of the training and carried out by the convolutional network, without the need for expert knowledge or handcrafted features. We compare our results against the recent state-of-the-art pipeline in which predefined features are extracted from each cell using specialized software and then fed into various machine learning algorithms (support vector machine, Fisher linear discriminant, random forest) for classification. The performance of all classification approaches is evaluated on an untouched test image set with known phenotype classes. Compared to the best reference machine learning algorithm, the misclassification rate is reduced from 8.9% to 6.6%.

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Spam Review Detection:A Systematic Literature Review

10.36227/techrxiv.12951077 ◽

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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Reviewing artificial intelligence methods for predictive modeling applications

10.31219/osf.io/ubpv9 ◽

2021 ◽

Author(s):

Andreas Sepp

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Predictive Modeling ◽

Significant Contribution ◽

Predictive Analytics ◽

State Of The Art ◽

Learning Methods ◽

Machine Learning Methods ◽

Artificial Intelligence Methods

Artificial intelligence and machine learning methods had significant contribution to the advancement and progress of predictive analytics. This article presents a state of the art of methods and applications of artificial intelligence and machine learning.

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A Review of Deep Machine Learning

International Journal of Engineering Research in Africa ◽

10.4028/www.scientific.net/jera.24.124 ◽

2016 ◽

Vol 24 ◽

pp. 124-136 ◽

Cited By ~ 15

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.

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Building a Real-Time 2D Lidar Using Deep Learning

Journal of Robotics ◽

10.1155/2021/6652828 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Nadim Arubai ◽

Omar Hamdoun ◽

Assef Jafar

Keyword(s):

Deep Learning ◽

Real Time ◽

Obstacle Avoidance ◽

Tilt Angle ◽

State Of The Art ◽

The State ◽

Prediction Problem ◽

Learning Methods ◽

Depth Prediction ◽

Avoidance Problem

Applying deep learning methods, this paper addresses depth prediction problem resulting from single monocular images. A vector of distances is predicted instead of a whole image matrix. A vector-only prediction decreases training overhead and prediction periods and requires less resources (memory, CPU). We propose a module which is more time efficient than the state-of-the-art modules ResNet, VGG, FCRN, and DORN. We enhanced the network results by training it on depth vectors from other levels (we get a new level by changing the Lidar tilt angle). The predicted results give a vector of distances around the robot, which is sufficient for the obstacle avoidance problem and many other applications.

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ARTIFICIAL INTELLIGENCE AND NEXT GENERATION PATHOLOGY: TOWARDS PERSONALIZED MEDICINE

Proceedings of the Shevchenko Scientific Society. Medical Sciences ◽

10.25040/ntsh2021.02.07 ◽

2021 ◽

Vol 65 (2) ◽

Author(s):

Oleksandr Dudin ◽

◽

Ozar Mintser ◽

Oksana Sulaieva ◽

◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Image Analysis ◽

Deep Learning ◽

Personalized Medicine ◽

Digital Pathology ◽

Automated Image Analysis ◽

Learning Methods ◽

Machine Learning Methods ◽

Wide Range

Introduction. Over the past few decades, thanks to advances in algorithm development, the introduction of available computing power, and the management of large data sets, machine learning methods have become active in various fields of life. Among them, deep learning possesses a special place, which is used in many spheres of health care and is an integral part and prerequisite for the development of digital pathology. Objectives. The purpose of the review was to gather the data on existing image analysis technologies and machine learning tools developed for the whole-slide digital images in pathology. Methods: Analysis of the literature on machine learning methods used in pathology, staps of automated image analysis, types of neural networks, their application and capabilities in digital pathology was performed. Results. To date, a wide range of deep learning strategies have been developed, which are actively used in digital pathology, and demonstrated excellent diagnostic accuracy. In addition to diagnostic solutions, the integration of artificial intelligence into the practice of pathomorphological laboratory provides new tools for assessing the prognosis and prediction of sensitivity to different treatments. Conclusions: The synergy of artificial intelligence and digital pathology is a key tool to improve the accuracy of diagnostics, prognostication and personalized medicine facilitation

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