Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have ...
Machine Learning: Algorithms, Real-World Applications and Research
In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI ...
Deep learning: systematic review, models, challenges, and research
The current development in deep learning is witnessing an exponential transition into automation applications. This automation transition can provide a promising framework for higher performance and lower complexity. This ongoing transition undergoes several rapid changes, resulting in the processing of the data by several studies, while it may lead to time-consuming and costly models. Thus ...
Top 10 Research and Thesis Topics for ML Projects in 2022
In this tech-driven world, selecting research and thesis topics in machine learning projects is the first choice of masters and Doctorate scholars. Selecting and working on a thesis topic in machine learning is not an easy task as machine learning uses statistical algorithms to make computers work in a certain way without being explicitly ...
Machine Learning Tech Giants: What are the Current Trends in Research
Machine Learning, particularly Deep Learning (DL) and Computer Vision, has seen remarkable growth. DL, a subset of ML, powers AI applications like Apple's Siri and Google's Assistant. Computer Vision, pivotal for image recognition, gained prominence with breakthroughs in 2012.
Could machine learning fuel a reproducibility crisis in science?
The pair analysed 20 reviews in 17 research fields, and counted 329 research papers whose results could not be fully replicated because of problems in how machine learning was applied 1.
Advancements and Challenges in Machine Learning: A Comprehensive Review
The following are the primary contributions of this paper: (1) an in-depth analysis of the current machine learning technique that is being applied to solve a wide range of classification, regression, and clustering issues. (2) The machine learning engineer can understand the reasoning behind all machine learning algorithms by reading this paper.
Challenges and opportunities in quantum machine learning
Abstract. At the intersection of machine learning and quantum computing, quantum machine learning has the potential of accelerating data analysis, especially for quantum data, with applications ...
Machine learning: Trends, perspectives, and prospects
A diverse array of machine-learning algorithms has been developed to cover the wide variety of data and problem types exhibited across different machine-learning problems (1, 2).Conceptually, machine-learning algorithms can be viewed as searching through a large space of candidate programs, guided by training experience, to find a program that optimizes the performance metric.
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Machine Learning is an international forum focusing on computational approaches to learning. Reports substantive results on a wide range of learning methods applied to various learning problems. Provides robust support through empirical studies, theoretical analysis, or comparison to psychological phenomena.
Here are the Most Common Problems Being Solved by Machine Learning
Compensate for missing data. Gaps in a data set can severely limit accurate learning, inference, and prediction. Models trained by machine learning improve with more relevant data. When used correctly, machine learning can also help synthesize missing data that round out incomplete datasets. Make more accurate predictions or conclusions from ...
Top 20 Recent Research Papers on Machine Learning and Deep Learning
Machine learning, especially its subfield of Deep Learning, had many amazing advances in the recent years, and important research papers may lead to breakthroughs in technology that get used by billio ns of people. The research in this field is developing very quickly and to help our readers monitor the progress we present the list of most important recent scientific papers published since 2014.
machine learning Latest Research Papers
Find the latest published documents for machine learning, Related hot topics, top authors, the most cited documents, and related journals ... and complexity of records and greater levels of disorganised data. Current practices for selecting records for transfer to The National Archives (TNA) were developed to deal with paper records and are ...
Current Research Questions in Machine Learning
Here are some current research questions / problems in Machine Learning that are required still need to do more work on these: Can unlabeled data be helpful for supervised learning? e.g., learning to classify webpages or spam ; How can we transfer what is learned for one task to improve learning in other related tasks? (Transfer Learning)
Machine Learning Research Problems
The Avenga Team. November 5, 2022. 10 min read. Technology trends. Decoding the complex world of AI research: a roadmap for businesses to navigate and capitalize on Machine Learning innovations. Machine learning research has changed. No one is no longer interested in applying machine learning methods and techniques to real-world problems.
Too many AI researchers think real-world problems are not relevant
To quote a classic paper titled "Machine Learning that Matters" (pdf), by NASA computer scientist Kiri Wagstaf f: "Much of current machine learning research has lost its connection to ...
Machine learning methods in finance: Recent applications and prospects
This paper addresses the use of ML to solve problems in finance research. Several overview papers indicate the potential of ML in finance. Varian ( 2014 ) describes ML as an appropriate tool in the economic analysis of big data and presents some ML methods with examples in economics.
9 Real-World Problems that can be Solved by Machine Learning
Spam detection is one of the best and most common problems solved by Machine Learning. Neural networks employ content-based filtering to classify unwanted emails as spam. These neural networks are quite similar to the brain, with the ability to identify spam emails and messages. 2.
A comprehensive review of predictive analytics models for mental
The use of machine learning in mental health care is an active area of research and development, and there is growing evidence to support its effectiveness. Recent review articles in the field of machine learning for mental health detection have focused on various aspects of the research landscape.
Top 20 Latest Research Problems in Big Data and Data Science
E ven though Big data is in the mainstream of operations as of 2020, there are still potential issues or challenges the researchers can address. Some of these issues overlap with the data science field. In this article, the top 20 interesting latest research problems in the combination of big data and data science are covered based on my personal experience (with due respect to the ...
Current progress and open challenges for applying deep learning across
Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper ...
(PDF) Current Advances, Trends and Challenges of Machine Learning and
Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI: Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross ...
Fairness issues, current approaches, and challenges in machine learning
With the increasing influence of machine learning algorithms in decision-making processes, concerns about fairness have gained significant attention. This area now offers significant literature that is complex and hard to penetrate for newcomers to the domain. Thus, a mapping study of articles exploring fairness issues is a valuable tool to provide a general introduction to this field. Our ...
7 Major Challenges Faced By Machine Learning Professionals
In this blog, we will discuss seven major challenges faced by machine learning professionals. Let's have a look. 1. Poor Quality of Data. Data plays a significant role in the machine learning process. One of the significant issues that machine learning professionals face is the absence of good quality data. Unclean and noisy data can make the ...
Machine Learning in Reservoir Engineering: A Review
Current machine learning-based research is focused on the following three aspects. (1) In the case of a lack of historical data in the pool, machine learning methods are mainly applied to select from geological aspects, as well as reservoir and fluid characteristics. ... The pros and cons of machine learning. The issues that the literature ...
Using Machine Learning to Understand Predictors of Frequent
This research sheds light on current surveillance patterns and may be helpful in designing interventions that deliver appropriate, value-based post-treatment care for CRC survivors. ... Machine Learning, Predictive Analytics, Clinical Outcomes Assessment, Electronic Medical & Health Records. Disease. Oncology, Personalized & Precision Medicine.
A review of traditional Chinese medicine diagnosis using machine
At present, the research on the automated diagnosis of TCM based on machine learning is booming. Machine learning, which includes deep learning, is an essential part of artificial intelligence (AI), which provides new ideas for the objective and AI-related research of TCM. This paper aims to review and summarize the current research status of ...
Meituan's Real-Time Intelligent Dispatching ...
Meituan's state-of-the-art real-time intelligent dispatch system harnesses the power of operations research and machine learning algorithms to fine-tune order assignments, simultaneously addressing the needs of consumers, couriers, merchants, and the platform itself. Over the past decade, Meituan, China's premier online food delivery (OFD) platform, has witnessed remarkable growth. Central ...
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Stay up-to-date with the AHA View All News The American Historical Review is the flagship journal of the AHA and the journal of record for the historical discipline in the United States, bringing together scholarship from every major field of historical study. Learn More Perspectives on History is the newsmagazine…
Machine learning in project analytics: a data-driven framework ...
This study proposes a machine learning-based data-driven research framework for addressing problems related to project analytics. It then illustrates an example of the application of this framework.
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Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have ...
In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI ...
The current development in deep learning is witnessing an exponential transition into automation applications. This automation transition can provide a promising framework for higher performance and lower complexity. This ongoing transition undergoes several rapid changes, resulting in the processing of the data by several studies, while it may lead to time-consuming and costly models. Thus ...
In this tech-driven world, selecting research and thesis topics in machine learning projects is the first choice of masters and Doctorate scholars. Selecting and working on a thesis topic in machine learning is not an easy task as machine learning uses statistical algorithms to make computers work in a certain way without being explicitly ...
Machine Learning, particularly Deep Learning (DL) and Computer Vision, has seen remarkable growth. DL, a subset of ML, powers AI applications like Apple's Siri and Google's Assistant. Computer Vision, pivotal for image recognition, gained prominence with breakthroughs in 2012.
The pair analysed 20 reviews in 17 research fields, and counted 329 research papers whose results could not be fully replicated because of problems in how machine learning was applied 1.
The following are the primary contributions of this paper: (1) an in-depth analysis of the current machine learning technique that is being applied to solve a wide range of classification, regression, and clustering issues. (2) The machine learning engineer can understand the reasoning behind all machine learning algorithms by reading this paper.
Abstract. At the intersection of machine learning and quantum computing, quantum machine learning has the potential of accelerating data analysis, especially for quantum data, with applications ...
A diverse array of machine-learning algorithms has been developed to cover the wide variety of data and problem types exhibited across different machine-learning problems (1, 2).Conceptually, machine-learning algorithms can be viewed as searching through a large space of candidate programs, guided by training experience, to find a program that optimizes the performance metric.
Machine Learning is an international forum focusing on computational approaches to learning. Reports substantive results on a wide range of learning methods applied to various learning problems. Provides robust support through empirical studies, theoretical analysis, or comparison to psychological phenomena.
Compensate for missing data. Gaps in a data set can severely limit accurate learning, inference, and prediction. Models trained by machine learning improve with more relevant data. When used correctly, machine learning can also help synthesize missing data that round out incomplete datasets. Make more accurate predictions or conclusions from ...
Machine learning, especially its subfield of Deep Learning, had many amazing advances in the recent years, and important research papers may lead to breakthroughs in technology that get used by billio ns of people. The research in this field is developing very quickly and to help our readers monitor the progress we present the list of most important recent scientific papers published since 2014.
Find the latest published documents for machine learning, Related hot topics, top authors, the most cited documents, and related journals ... and complexity of records and greater levels of disorganised data. Current practices for selecting records for transfer to The National Archives (TNA) were developed to deal with paper records and are ...
Here are some current research questions / problems in Machine Learning that are required still need to do more work on these: Can unlabeled data be helpful for supervised learning? e.g., learning to classify webpages or spam ; How can we transfer what is learned for one task to improve learning in other related tasks? (Transfer Learning)
The Avenga Team. November 5, 2022. 10 min read. Technology trends. Decoding the complex world of AI research: a roadmap for businesses to navigate and capitalize on Machine Learning innovations. Machine learning research has changed. No one is no longer interested in applying machine learning methods and techniques to real-world problems.
To quote a classic paper titled "Machine Learning that Matters" (pdf), by NASA computer scientist Kiri Wagstaf f: "Much of current machine learning research has lost its connection to ...
This paper addresses the use of ML to solve problems in finance research. Several overview papers indicate the potential of ML in finance. Varian ( 2014 ) describes ML as an appropriate tool in the economic analysis of big data and presents some ML methods with examples in economics.
Spam detection is one of the best and most common problems solved by Machine Learning. Neural networks employ content-based filtering to classify unwanted emails as spam. These neural networks are quite similar to the brain, with the ability to identify spam emails and messages. 2.
The use of machine learning in mental health care is an active area of research and development, and there is growing evidence to support its effectiveness. Recent review articles in the field of machine learning for mental health detection have focused on various aspects of the research landscape.
E ven though Big data is in the mainstream of operations as of 2020, there are still potential issues or challenges the researchers can address. Some of these issues overlap with the data science field. In this article, the top 20 interesting latest research problems in the combination of big data and data science are covered based on my personal experience (with due respect to the ...
Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper ...
Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI: Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross ...
With the increasing influence of machine learning algorithms in decision-making processes, concerns about fairness have gained significant attention. This area now offers significant literature that is complex and hard to penetrate for newcomers to the domain. Thus, a mapping study of articles exploring fairness issues is a valuable tool to provide a general introduction to this field. Our ...
In this blog, we will discuss seven major challenges faced by machine learning professionals. Let's have a look. 1. Poor Quality of Data. Data plays a significant role in the machine learning process. One of the significant issues that machine learning professionals face is the absence of good quality data. Unclean and noisy data can make the ...
Current machine learning-based research is focused on the following three aspects. (1) In the case of a lack of historical data in the pool, machine learning methods are mainly applied to select from geological aspects, as well as reservoir and fluid characteristics. ... The pros and cons of machine learning. The issues that the literature ...
This research sheds light on current surveillance patterns and may be helpful in designing interventions that deliver appropriate, value-based post-treatment care for CRC survivors. ... Machine Learning, Predictive Analytics, Clinical Outcomes Assessment, Electronic Medical & Health Records. Disease. Oncology, Personalized & Precision Medicine.
At present, the research on the automated diagnosis of TCM based on machine learning is booming. Machine learning, which includes deep learning, is an essential part of artificial intelligence (AI), which provides new ideas for the objective and AI-related research of TCM. This paper aims to review and summarize the current research status of ...
Meituan's state-of-the-art real-time intelligent dispatch system harnesses the power of operations research and machine learning algorithms to fine-tune order assignments, simultaneously addressing the needs of consumers, couriers, merchants, and the platform itself. Over the past decade, Meituan, China's premier online food delivery (OFD) platform, has witnessed remarkable growth. Central ...
Stay up-to-date with the AHA View All News The American Historical Review is the flagship journal of the AHA and the journal of record for the historical discipline in the United States, bringing together scholarship from every major field of historical study. Learn More Perspectives on History is the newsmagazine…
This study proposes a machine learning-based data-driven research framework for addressing problems related to project analytics. It then illustrates an example of the application of this framework.