Machine Learning

Summary

  • 12 945 Companies
  • 1 306 Patents
  • 33 698 Use Cases
  • 1 127 Case Studies
  • 59 339 Science Papers
  • $11 707 590 972 Total Funding

Companies

#Organisation NameIndustriesHeadquarterDescriptionFounded YearCompany TypeNum of Employees
1
Information Technology
Mannarkkad, KERALA
Infolks is India's fastest growing outsourcing company with a simple motto “outstanding outsourcing”, majored in all kinds of data labeling services. We are ISO 9001:2015 certified and offer high-quality service for machine learning applications requiring training data. At Infolks, a dedicated team of professionals and data labelers committed to client satisfaction through their performance. Our expertise range over several annotation techniques like Bounding Box, Keypoint, Cuboid, Polygonal, and Polyline Annotation. We are ISO 27001:2013 certified and follow GDPR standards. Other outsourcing services include, Out staffing, Redaction, Software solutions, and BPO. We are providing the best quality services at the lowest hourly rate in the global marketplace.
2016
Privately Held
515
2
Marketing and Advertising
Redwood City, California
MOLOCO is a machine learning company that empowers mobile businesses to unleash the power of their data for fast, sustainable growth through the programmatic advertising ecosystem. Founded in 2013, the company offers a complete suite of programmatic advertising solutions to help mobile companies optimize the performance of their acquisition, retention and monetization campaigns. MOLOCO offers a top-rated real-time bidding platform that provides scale across more than 4 billion devices. Through highly sophisticated products like MOLOCO Cloud and MOLOCO Engine, consumers are able to easily utilize the benefits of their own data while leveraging the power of MOLOCO’s proprietary machine learning technology. MOLOCO is headquartered in Silicon Valley, with offices in San Francisco, Seattle, London, Seoul, Singapore, and Tokyo. For more information, visit www.moloco.com.
2013
Privately Held
354
3
Information Technology
Minneapolis, MN
phData is the perfect mix of services and automation to create solid data platforms, outstanding data products, and value-generating machine learning systems in the cloud. phData guides the world’s largest brands in cloud data platforms, data engineering, data science, and machine learning.
2014
Privately Held
337
4
Information Technology
Charlotte, NC
Mariner provides products and services for better decision-making to manufacturers, worldwide. We offer Spyglass, a family of AI and IoT products: Spyglass Visual Inspection - harnesses the power of deep learning, IIoT, and AI to help manufacturers extend the life of their machine/computer vision investment. Spyglass Visual Inspection improves the accuracy of machine vision systems. Spyglass Connected Factory - empowers manufacturers to monitor, control, and predict crucial aspects of the manufacturing process in real time. With Spyglass Connected Factory, manufacturers can effectively reduce unplanned downtime, improve product quality, and optimize production processes – ultimately reducing operating costs. Spyglass Connected Product - includes cloud-based services that can be implemented quickly and efficiently. Using the Internet of Things to create recurring revenue streams, improve field service efficiency, increase customer satisfaction and improve product engineering. Spyglass listens to your products and speaks to you, helping your customers productivity and helping you improve profitability to help you beat the competition. Mariner specializes in creating resilient machine learning models that we integrate into customers’ business processes with the fastest time to value.
1998
Privately Held
237
5
Software
Seattle, WA
Get more from your machine learning models. OctoML automatically accelerates machine learning model performance without sacrificing accuracy while also enabling seamless deployment.
2019
Privately Held
125
6
Internet
San Francisco, California
Functionize delivers an intelligent testing platform that incorporates AI and machine learning technologies to automate the painstaking software testing process. We work with teams of all sizes and skill sets to improve test creation, eliminate test flakiness and accelerate releases with elastic scale testing in the cloud. The Functionize cloud-based platform enables enterprise teams to rapidly accelerate software development while eliminating traditional test scripts, test maintenance and the need for a large QA team. https://www.functionize.com Follow us on Twitter at @functionize
2014
Privately Held
119
7
Software
Ghent, Flemish Region
WE ARE HIRING! ML6 are a team of machine learning experts who have been driving operational efficiency and value creation through self learning systems since 2013. We believe that our uniqueness stems from our emphasis on staying on top of research, being innovative, cultivating talent and applying our research in practice. ML6 is trusted by global players in the retail & e-commerce, manufacturing, media, healthcare and financial services industry. Our company is spread across 4 locations in Europe and are a specialized machine learning partner for Google Cloud. Please visit www.ML6.eu for more information.
2013
Privately Held
103
8
Software
San Francisco, California
Founded by the team that created the Uber Michelangelo platform, Tecton provides an enterprise-ready feature store to make world-class machine learning accessible to every company. Machine learning creates new opportunities to generate more value than ever before from data. Companies can now build ML-driven applications to automate decisions at machine speed, deliver magical customer experiences, and re-invent business processes. But ML models will only ever be as good as the data that is fed to them. Today, it’s incredibly hard to build and manage ML data. Most companies don’t have access to the advanced ML data infrastructure that is used by the internet giants. So ML teams spend the majority of their time building custom features and bespoke data pipelines, and most models never make it to production. We believe that companies need a new kind of data platform built for the unique requirements of ML. Our goal is to enable ML teams to build great features, serve them to production quickly and reliably, and do it at scale. By getting the data layer for ML right, companies can get better models to production faster to drive real business outcomes.
2019
Privately Held
91
9
Software
New York, NY
Comet is a meta machine learning platform designed to help AI practitioners and teams build reliable machine learning models for real-world applications by streamlining and connecting the machine learning model lifecycle. By leveraging Comet, users can track, compare, explain and reproduce their machine learning experiments. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.
2017
Privately Held
86
10
Information Technology
Ciudad de Buenos Aires, Buenos Aires (Ciudad)
Mutt Data is a technology company that helps startups and big companies build and implement Machine Learning solutions that drive real business results. Whether you work in finance, insurance, advertising, telcos, on-demand services or e-commerce, our solutions will help you get ahead of your competition with the latest technologies, techniques and best practices
2017
Privately Held
74

Patents

#NumberTitleAbstractDateKindAssigneeInventor
1
11 048 933
Generating structured representations of forms using machine learning
A method may include acquiring, from a document, document elements and attributes describing the document elements. One or more of the attributes may be geometric attributes describing a placement of the corresponding document element within the document. The method may further include deriving features for the document elements using the attributes, detecting form components using the features, clustering the form components into line objects of a structured representation by applying an unsupervised machine learning model to the geometric attributes of the document elements, and populating a compliance form using the structured representation.
B2
Intuit Inc.
Anu Singh, Karpaga Ganesh Patchirajan, Saikat Mukherjee, Mritunjay Kumar
2
11 049 012
Explaining machine learning models by tracked behavioral latent features
A system and method to explain model behavior, which can benefit not only those seeking to meet regulatory requirements when using machine learning models but also help guide users of the model to assess and increase robustness associated with model governance processes. The method described utilizes changes in behavior of a time series to identify the latent factors that drive explanation.
B2
Fair Isaac Corporation
Chahm An, Scott Michael Zoldi
3
11 048 382
Scanning system, scanning program, and machine learning system
A scanning system includes: a configurator configured to make a first setting corresponding to an image captured by a pre-scan the setting of a process accompanying scanning based on learning results acquired by machine learning using training data related to a setting of the process accompanying scanning that is applied to a scanned image; and an executor that executes the process accompanying scanning based on the first setting.
B2
Seiko Epson Corporation
Yo Kawano, Toshifumi Sakai
4
11 048 793
Dynamically generating activity prompts to build and refine machine learning authentication models
Aspects of the disclosure relate to dynamically generating activity prompts to build and refine machine learning authentication models. A computing platform may process a first set of login events associated with a first user account and may build a first user-specific authentication model for the first user account. Then, the computing platform may process a second set of login events associated with a second user account and may build a second user-specific authentication model for the second user account. The computing platform also may build a population-level authentication model for a plurality of user accounts. Thereafter, the computing platform may identify one or more activity parameters associated with at least one authentication model for refinement. Subsequently, the computing platform may generate and send one or more activity prompts to one or more client computing devices to request at least one user response.
B2
Bank of America Corporation
Hitesh Shah, Michael E. Toth
5
11 048 972
Machine learning based system for identifying resonated connections in online connection networks
The technical problem of identifying relevant social proof information with respect to a premium service for a given member profile in an online connection network system is addressed by, first, capturing the associated member's intent based on the member's activity on the web site provided by the online connection network system. The determined intent is used as input into a relevance machine learning model that is executed to identify the member's connection who is a subscriber to the premium service and who has been identified as the most convincing resonated connection of the member with respect to subscribing to the premium service.
B2
Microsoft Technology Licensing, LLC
Alexander Shoykhet, Ying Xi, Bing Wang, Yan Liu, Ajita Thomas
6
11 049 009
Identifying memory block write endurance using machine learning
Systems and methods are described for predicting an endurance of groups of memory cells within a memory device, based on current characteristics of the cells. The endurance may be predicted by processing historical information regarding operation of memory devices according to a machine learning algorithm, such as a neural network algorithm, to generate correlation information between characteristics of groups of memory calls at a first time and an endurance metric at a second time. The correlation information can be applied to current characteristics of a group of memory cells to predict a future endurance of that group. Operating parameters of a memory device may be modified at a per-block level based on predicted block endurances to increase the speed of a device, the longevity of a device, or both.
B2
Western Digital Technologies, Inc.
Alexander Kalmanovich, Ariel Navon, Arthur Shulkin, David Rozman
7
11 045 949
Deep machine learning methods and apparatus for robotic grasping
Deep machine learning methods and apparatus related to manipulation of an object by an end effector of a robot. Some implementations relate to training a semantic grasping model to predict a measure that indicates whether motion data for an end effector of a robot will result in a successful grasp of an object; and to predict an additional measure that indicates whether the object has desired semantic feature(s). Some implementations are directed to utilization of the trained semantic grasping model to servo a grasping end effector of a robot to achieve a successful grasp of an object having desired semantic feature(s).
B2
GOOGLE LLC
Eric Jang, Sudheendra Vijayanarasimhan, Peter Pastor Sampedro, Sergey Levine
8
11 048 215
Tool selecting apparatus and machine learning device
A machine learning device included in a tool selecting apparatus includes a state observing unit that observes, as state variables indicative of a current environmental state, data related to machining condition, data related to cutting condition, data related to machining result, and data related to a tool, and a learning unit that, by using the state variables, learns distribution of the data related to the machining condition, the data related to the cutting condition, and the data related to the machining result, with respect to data related to the tool.
B2
FANUC CORPORATION
Takuya Saitou
9
11 048 641
Managing allocation and demotion of cache segments between a global queue and a plurality of local queues by using a machine learning module
Provided are a computer program product, system, and method for managing cache segments between a global queue and a plurality of local queues using a machine learning module. Cache segment management information related to management of segments in the local queues and accesses to the global queue to transfer cache segments between the local queues and the global queue, are provided to a machine learning module to output an optimum number parameter comprising an optimum number of segments to maintain in a local queue and a transfer number parameter comprising a number of cache segments to transfer between a local queue and the global queue. The optimum number parameter and the transfer number parameter are sent to a processing unit having a local queue to cause the processing unit to transfer the transfer number parameter of cache segments between the local queue to the global queue.
B2
International Business Machines Corporation
Kevin J. Ash, Matthew R. Craig, Beth A. Peterson, Lokesh M. Gupta
10
11 049 021
System and method for compact tree representation for machine learning
Aspects of the present disclosure involve systems, methods, devices, and the like for generating compact tree representations applicable to machine learning. In one embodiment, a system is introduced that can retrieve a decision tree structure to generate a compact tree representation model. The compact tree representation model may come in the form of a matrix design to maintain the relationships expressed by the decision tree structure.
B2
PayPal, Inc.
Michael Dymshits, David Tolpin, Raoul Christopher Johnson, Omri Moshe Lahav

Patents by Year

Inventors

Assignees

Assignees

Science

Data limited by 2021

Top 10 cited papers

#Paper TitlePaper AbstractAuthorsFields of StudyYearCitation Count
1
Scikit-learn: Machine Learning in Python
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.
Computer Science
2011
43 274
2
C4.5: Programs for Machine Learning
From the Publisher: Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use , the source code (about 8,800 lines), and implementation notes. The source code and sample datasets are also available on a 3.5-inch floppy diskette for a Sun workstation. C4.5 starts with large sets of cases belonging to known classes. The cases, described by any mixture of nominal and numeric properties, are scrutinized for patterns that allow the classes to be reliably discriminated. These patterns are then expressed as models, in the form of decision trees or sets of if-then rules, that can be used to classify new cases, with emphasis on making the models understandable as well as accurate. The system has been applied successfully to tasks involving tens of thousands of cases described by hundreds of properties. The book starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Advantages and disadvantages of the C4.5 approach are discussed and illustrated with several case studies. This book and software should be of interest to developers of classification-based intelligent systems and to students in machine learning and expert systems courses.
Computer Science
1992
22 006
3
Machine learning
Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
Computer Science
1996
15 646
4
TensorFlow: A system for large-scale machine learning
TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Tensor-Flow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous "parameter server" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real-world applications.
Computer Science
2016
12 461
5
Data mining: practical machine learning tools and techniques, 3rd Edition
Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
Computer Science
1999
12 331
6
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. The TensorFlow API and a reference implementation were released as an open-source package under the Apache 2.0 license in November, 2015 and are available at www.tensorflow.org.
Computer Science
2016
9 199
7
Machine learning in automated text categorization
The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.
Computer Science
2002
8 512
8
Thumbs up? Sentiment Classification using Machine Learning Techniques
We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging.
Computer Science
2002
8 498
9
Machine learning - a probabilistic perspective
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Computer Science
2012
7 072
10
Programs for Machine Learning
Algorithms for constructing decision trees are among the most well known and widely used of all machine learning methods. Among decision tree algorithms, J. Ross Quinlan's ID3 and its successor, C4.5, are probably the most popular in the machine learning community. These algorithms and variations on them have been the subject of numerous research papers since Quinlan introduced ID3. Until recently, most researchers looking for an introduction to decision trees turned to Quinlan's seminal 1986 Machine Learning journal article [Quinlan, 1986]. In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments. As such, this book will be a welcome addition to the library of many researchers and students.
Computer Science
1994
6 949

Top 10 cited authors

#AuthorPapers countCitation Count
1
7
61 495
2
14
46 106
3
5
45 903
4
6
45 633
5
16
45 286
6
11
45 073
7
4
44 870
8
2
44 589
9
3
44 589
10
10
44 455

Science papers by Year

Clinical Trials

  • Researches Count 130
  • Ongoing Studies 63
  • Total Enrollment 2 301 339

Clinical Trials by Year

Countries

Phases

Clinical Trials

#TitleConditionsInterventionsEnrollmentYearLocations
1
Gram Type Infection-Specific Sepsis Identification Using Machine Learning
Sepsis, Septic Shock, Severe Sepsis
InSight
0
2022
Dascena
2
Unsupervised Machine Learning for Clustering of Septic Patients to Determine Optimal Treatment
Sepsis, Septic Shock, Severe Sepsis
InSight, Treatment-specific InSight
51 645
2022
Dascena
3
Multimodal PET/MRI Machine Learning Approaches for Characterization of Solid Tumors
Adenocarcinoma of Prostate, Glioma, Hepatocellular Carcinoma (HCC), Prostate Cancer, Radical Prostatectomy, Renal Cell Carcinoma (RCC)
[18F]DCFPyL, PET/MRI scanner
135
2022
Massachusetts General Hospital
4
A Machine Learning Approach for Predicting tDCS Treatment Outcomes of Adolescents With Autism Spectrum Disorders
Autistic Disorders Spectrum, Electroencephalography, Machine Learning, Transcranial Direct Current Stimulation
tDCS
90
2022
The Hong Kong Polytechnic University
5
Generalizable Machine Learning to Predict Acute Care During Outpatient Systemic Cancer
Chemotherapeutic Toxicity
Machine learning algorithm
12 000
2022
Duke University
6
Use of Natural Language Processing (NLP) and Machine Learning (ML) for the Identification of Patients With Crohn's Disease (CD) and Complex Perianal Fistulas (CPF) and Their Characterization in Terms of Clinical and Demographic Characteristics. A Multicentre, Retrospective, NLP Based Study
Crohn Disease, Rectal Fistula
100
2022
Takeda
7
Telemedicine Notifications With Machine Learning for Postoperative Care
Acute Kidney Injury, Hospital Mortality, Perioperative/Postoperative Complications, Surgery--Complications
Anesthesia Control Tower Notification
3 375
2022
Washington University School of Medicine
8
Effect of a Machine Learning Ventilator Decision System Versus Standard Controlled Ventilation on in Critical Care: a Randomized Trial
Critically Ill Patients, Mechanical Ventilation
Machine Learning Ventilator Decision System
300
2022
The Second Clinical Medical College of Jinan University
9
Development, Feasibility and Effectiveness of a Digital Support Platform for Mental Health in Primary Care (PRESTO) Based on a Machine Learning Approach.
Depression, Anxiety
PRESTOapp
152
2021
Hospital Clinic of Barcelona
10
Evaluation of the Use of Machine Learning Techniques to Classify Neurodegenerative PARKinsonian Syndromes (Artificial Intelligence)
DaTSCAN SPECT Scans
1 664
2021
Central Hospital, Nancy, France

Trends

#LinkTrendsRank
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Use Cases

#TopicPaper TitleYearFields of studyCitationsUse CaseAuthors
1
Machine Learning
Use of machine learning to detect Lung Cancer
2022
0
detect lung cancer
2
Machine Learning
Prediction of Binder Content in Glass Fiber Reinforced Asphalt Mix Using Machine Learning Techniques
2022
Materials Science, Computer Science
0
prediction of binder content in glass fiber reinforced asphalt mix
3
Machine Learning
Hate Speech Detection on Social Media Using Machine Learning Algorithms
2022
0
hate speech detection on social media
4
Machine Learning
An Extensive Examination of Discovering 5-Methylcytosine Sites in Genome-Wide DNA Promoters Using Machine Learning Based Approaches
2022
Medicine, Computer Science, Biology
1
an extensive examination of discovering 5-methylcytosine sites in genome-wide dna promoters
5
Machine Learning
Applying Machine Learning to Finger Movements Using Electromyography and Visualization in Opensim
2022
Medicine, Computer Science
0
finger movements using electromyography and visualization in opensim
6
Machine Learning
Condition Classification of Fibre Ropes during Cyclic Bend over Sheave testing Using Machine Learning
2022
0
condition classification of fibre ropes during cyclic bend over sheave testing
7
Machine Learning
Determination of the impact parameter distribution of low-intermediate energy heavy ion collisions using machine learning
2022
0
determination of the impact parameter distribution of low-intermediate energy heavy ion collisions
8
Machine Learning
Prediction of hospital mortality in mechanically ventilated patients with congestive heart failure using machine learning approaches.
2022
Medicine
0
prediction of hospital mortality in mechanically ventilated patients with congestive heart failure
9
Machine Learning
Vibration analysis for fault detection in wind turbines using machine learning techniques
2022
0
vibration analysis for fault detection in wind turbines
10
Machine Learning
Performance evaluation of machine learning for fault selection in power transmission lines
2022
Computer Science
0
fault selection in power transmission lines

Case Studies

#TitleDescriptionPDFYearSource Ranking
1
Evaluating Privacy-Preserving Machine Learning in Critical ...
Feb 9, 2022 — Medical Images. MelbournePedestrian. NonIn. v. asi. v. eFetalECGThorax1. PhalangesOutlinesCorrect. Strawberry. UW. a. v. eGestureLibraryAll.
no
2022
6 650
2
Machine Learning Assisted Interpretation: Integrated Fault ...
by S Smith · 2022 — https://mc.manuscriptcentral.com/interpretation. Interpretation. This paper presented here as accepted for publication in Interpretation ...
yes
2022
100
3
Analysis of Healthcare Industry Using Machine Learning ...
by P Taranath · 2022 — Analysis of Healthcare Industry Using Machine Learning Approach: A Case Study in Bengaluru Region. Authors; Authors and affiliations. Poornima ...
no
2022
2 670
4
Machine-learning assisted interpretation: Integrated fault ...
by S Smith · 2022 — extraction case study from the Groningen gas field, Netherlands ... faulted, subsalt gas field located onshore, northeast Netherlands.
yes
2022
100
5
Machine Learning Architecture for Heart Disease Detection
by BJ Saleh · 2022 — Title: Machine Learning Architecture for Heart Disease Detection: A Case Study in Iraq. Language: English; Authors: Saleh, Basma Jumaa1 eng.basmaj ...
no
2022
620
6
Machine Learning Case Study — Credit Card Fraud Detection
Feb 12, 2022 — It would not be wrong to say, that payment cards like credit/debit cards have become a lifeline for almost all of us.
no
2022
6 050
7
Machine Learning Case Study — Credit Card Fraud Detection
Feb 12, 2022 — It would not be wrong to say, that payment cards like credit/debit cards have become a lifeline for almost all of us.
no
2022
6 050
8
Grade Control with Ensembled Machine Learning - SpringerLink
Mar 3, 2022 — The methodology implemented in this case study uses machine learning algorithms to model copper grade, which is incorporated in an intrinsic ...A Case Study of Auto-sklearn for Traffic Forecasting - Springerhttps://link.springer.com › chapterhttps://link.springer.com › chapter
no
2022
2 670
9
Machine Learning Architecture for Heart Disease Detection
by BJ Saleh · 2022 — Department of Computer Engineering, Mustansiriyah University, Baghdad, Iraq [email protected] Abstract—In recent years, the amount of data ...
no
2022
620
10
Machine Learning Linear Regression Case Study - AVAXGFX
Jan 14, 2022 — Updated 1/2021 | Size: 395 MB. Predicting Boston house price with Linear Regression using scikit-learn !! What you'll learn
no
2022
120

Experts

Twitter

#NameDescriptionFollowersFollowingLocation
1
TensorFlow
TensorFlow is a fast, flexible, and scalable open-source machine learning library for research and production.
308 850
117
Mountain View, CA
2
Bot Sentinel
Bot Sentinel is a free platform developed to classify and track inauthentic accounts and toxic trolls using machine learning and artificial intelligence.
201 141
3
United States
3
Ward Plunet
Phd in Neuroscience looking at the intersection between machine learning and neuroscience #machinelearning #AI #neuroscience
136 599
114 449
Vancouver, Canada
4
Deepti Gurdasani
Senior Lecturer @QMUL Epidemiology, statistical genetics, machine learning. Intersectional feminist. Advocating for a better culture in academia. All views mine
123 067
4 291
-
5
Santiago
I write about Machine Learning • Practical tips and epic stories about my experience in the field.
121 838
310
🇺🇸
6
Stanford NLP Group
Computational Linguistics—Natural Language—Machine Learning @chrmanning @jurafsky @percyliang @ChrisGPotts @tatsu_hashimoto @MonicaSLam @StanfordAILab Stanford
109 468
99
Stanford, CA, USA
7
Hugo Larochelle
Google Brain researcher, machine learning professor, ex-Twitter Cortex, father of 4, wine/music/comedy enthusiast
98 861
580
-
8
Rachel Thomas
co-founder https://t.co/AX7b3ucnOb, professor of practice @QUTDataScience | machine learning, data, ethics, algorithmic harms, math PhD (she/her)
87 136
837
Brisbane, Australia
9
Arman Anwar
Builds thinking machines that print money - Statistical Machine Learning Geek - Good-old-Fashioned AI Nostalgic - Recreational Mathematician - Stares at Art
75 232
112
Manhattan, NY
10
Sebastian Raschka
I tweet about Python & deep learning! Lead AI Educator @gridai_. Asst Prof of Statistics @UWMadison. Author of "Machine Learning with PyTorch and Scikit-Learn."
70 629
580
Madison, Wisconsin

Quora Profiles

#NameAnswersFollowersLocationViewsTopicTopic LinkAnswers to topic
1
19036
16 321
The United States of America
39 425 776
Machine Learning
19036
2
9776
11 241
Atlanta, GA
32 506 463
Machine Learning
9776
3
1030
19 482
Moscow
10 823 026
Machine Learning
1030
4
1092
7 758
The United States of America
8 430 296
Machine Learning
1092
5
888
2 123
8 090 642
Machine Learning
888
6
1353
15 379
6 983 943
Machine Learning
1353
7
1171
6 879
Lusaka, Zambia
4 888 460
Machine Learning
1171
8
174
42 148
Montréal, QC, Canada
4 182 138
Machine Learning
174
9
785
9 009
3 803 279
Machine Learning
785
10
61
1 734
Houston, TX
3 167 273
Machine Learning
61

Youtube Channels

#NameDescriptionReg DateViewsCountry
1
I am the cofounder of iNeuron and my experience is pioneering in machine learning, deep learning, and computer vision,an educator, and a mentor, with over 10 years' experience in the industry. This is my YouTube channel where I explain various topics on machine learning, deep learning, and AI with many real-world problem scenarios. I have delivered over 30 tech talks on data science, machine learning, and AI at various meet-ups, technical institutions, and community-arranged forums. My main aim is to make everyone familiar of ML and AI.Please subscribe and support the channel. As i love new technology, all these videos are free and I promise to make more interesting content as we go ahead. For any collaboration drop me a mail at [email protected] Please free to drop a mail for Product unboxing, GPU's unboxing and any other collaboration
Thu, 9 Feb 2012
46 961 174
India
2
Welcome to Code Parade! This is where I'll upload interesting projects and experiments that I do for fun. Topics will usually involve machine learning, games, algorithms, fractals, or any other topics I find interesting. I usually include some explanations, and sometimes tutorials. If you have any ideas for projects, especially machine learning ones, feel free to post them on my channel discussion page!
Fri, 11 Aug 2017
33 997 023
United States
3
Ein Kanal rund um das Thema Informatik und Programmieren mit über 2000 Videos! Ob Python, Java oder Webdevelopment oder doch Hacken und theoretische Informatik - es ist alles da! Für Wünsche bin ich jederzeit offen, also schreibt einfach Kommentare bzw besucht die Website um mir Wünsche mitzuteilen. Zu mir: Ich, Cedric oder Morpheus (sucht's euch aus) habe einen Master in allgemeiner Informatik mit Schwerpunkten Machine Learning und IT-Sicherheit und bilde mich in meiner Freizeit ständig weiter, daher auch nicht ganz unwissend in all den Informatik-Themen. Dankbar bin ich natürlich auch über jede Unterstützung, seien es Likes und Shares oder Spenden via Patreon oder Paypal. Impressum: Cedric Mössner c/o RA Matutis Berliner Straße 57 14467 Potsdam E-Mail: [email protected] Telefon: +49 7631 9759 803 Verantwortlich für redaktionelle Inhalte Cedric Mössner Umsatzsteuer-Identifikationsnummer DE327206699 Datenschutz/Privacy: https://www.the-morpheus.de/privacy_socialmedia.html
Sat, 23 Jul 2011
28 445 758
Germany
4
I simply love claw machines. I love the business side, I love to play. I purchased one of my very own and now would love to both share what I have learned from years of playing as well as learn from you, the claw community. My good friend Brandon and I started this channel to help feed your hobby, become a better player, fix your own machine, learn which machines to avoid, and have some fun in the process We show you how to beat claw machines, how to win ticket games, do arcade tours and even more! Hope You enjoy the channel! Subscribe for updates when we push new arcade videos!
Sun, 23 Nov 2014
19 008 975
United States
5
Moises Sanabria: Machine Learning & Art Direction.
Sun, 1 Jan 2012
13 063 018
6
QNAP Systems, Inc., headquartered in Taipei, Taiwan, provides a comprehensive range of cutting-edge Network-attached Storage (NAS) and video surveillance solutions based on the principles of usability, high security, and flexible scalability. QNAP offers quality NAS products for home and business users, providing solutions for storage, backup/snapshot, virtualization, teamwork, multimedia, and more. QNAP envisions NAS as being more than "Simple storage", and has created many NAS-based innovations to encourage users to host and develop Internet of Things, artificial intelligence, and machine learning solutions on their QNAP NAS.
Tue, 5 Aug 2008
7 326 672
Taiwan
7
Hi! I'm Calle, a Swedish guy from Växjö/Lund. I'm deeply in love with all forms of creativity and tech-savvyness. I work as a programmer in data science and machine learning, but in my free time I love express my more creative side. Writing music, producing videos, vfx, painting etc. Some of the stuff end up here on youtube, but most is just left as unfinished ideas on my computer.
Mon, 25 Feb 2008
7 182 208
Sweden
8
Machine learning channel
Mon, 3 Oct 2016
7 047 542
United States
9
Free Python and Machine Learning Tutorials! Hi, I'm Patrick. I’m a passionate Software Engineer who loves Machine Learning, Computer Vision, and Data Science. I create free content in order to help more people get into those fields. If you have any questions, feedback, or comments, just shoot me a message! I am happy to talk to you :) If you like my content, please subscribe to the channel! Please check out my website for more information: https://www.python-engineer.com If you find these videos useful and would like to support my work you can find me on Patreon: https://www.patreon.com/patrickloeber Legal: https://www.python-engineer.com/legal-notice/
Thu, 2 May 2019
5 240 102
10
I'm a machine learning engineer who plays at the intersection of technology and health. My videos will help you learn better and live healthier. Feel free to introduce yourself, I'd love to hear from you. Daniel
Mon, 1 Aug 2016
5 089 618
Australia