ML-based Applications Research Articles

Artificial Learning and Machine Learning Decision Guidance Applications in Total Hip and Knee Arthroplasty: A Systematic Review.

BackgroundArtificial intelligence (AI) and machine learning (ML) modeling in hip and knee arthroplasty (total joint arthroplasty [TJA]) is becoming more commonplace. This systematic review aims to quantify the accuracy of current AI- and ML-based application for cognitive support and decision-making in TJA.MethodsA comprehensive search of publications was conducted through the EMBASE, Medline, and PubMed databases using relevant keywords to maximize the sensitivity of the search. No limits were placed on level of evidence or timing of the study. Findings were reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Analysis of variance testing with post-hoc Tukey test was applied to compare the area under the curve (AUC) of the models.ResultsAfter application of inclusion and exclusion criteria, 49 studies were included in this review. The application of AI/ML-based models and average AUC is as follows: cost prediction-0.77, LOS and discharges-0.78, readmissions and reoperations-0.66, preoperative patient selection/planning-0.79, adverse events and other postoperative complications-0.84, postoperative pain-0.83, postoperative patient-reported outcomes measures and functional outcome-0.81. Significant variability in model AUC across the different decision support applications was found (P < .001) with the AUC for readmission and reoperation models being significantly lower than that of the other decision support categories.ConclusionsAI/ML-based applications in TJA continue to expand and have the potential to optimize patient selection and accurately predict postoperative outcomes, complications, and associated costs. On average, the AI/ML models performed best in predicting postoperative complications, pain, and patient-reported outcomes and were less accurate in predicting hospital readmissions and reoperations.

Economic Aspects of Introducing Artificial Intelligence Solutions in Logistics and Port Sectors: The Data Entry Case

The development and implementation of digital solutions are new in contemporary businesses in logistics. As a next step, the potential of advanced solutions that make use of an AI or ML algorithm and which leverage on data is highly promoted. Yet, the implementation on a large scale of these types of solutions is happening at a slow pace. Recent studies show that a considerable amount of data in the maritime supply chain (MarSC) is still transferred through traditional communication channels (e.g., via e-mails or attached xls, pdf, csv, xml, etc. documents). Human intervention is thus needed to fetch this information and type it over in internal ERP systems. This type of practice opens the scene for extra labor, misinterpretation, or faults. This research puts forward the port users’ perspective on the implementation of AI and ML-based applications for the automatic handling of data. To achieve this goal, a structured survey is launched. The survey results show that, while AI and ML technologies have a high potential to take over repetitive and fault-sensitive tasks, human operators are still needed to maintain customer relations or carry out other planning-related tasks. This initial inquiry shows that, although there are operational costs that are avoided by AI-based technologies, the logistics sector shows low willingness to pay or join development tracks for this type of solutions.

Random-forest based adjusting method for wind forecast of WRF model

Nowadays, machine learning (ML) methods have gained much attention and have been applied in some important related applications in earth science field, including observation data mining, geoscience image recognition, remote sensing image classification and so on. These ML-based applications play important roles in our daily life. However, in meteorological and oceanographic forecast, numerical is still the most popular method. Although researchers have proposed some ML-based prediction methods to overcome the shortcomings of numerical weather forecast methods, the explainability for the forecast result of artificial intelligence (AI) technology is still not as good as numerical weather forecast methods. Therefore, in this paper, we propose a random forest based adjusting method, which introduces AI technology to correct wind prediction results of numerical model. The proposed adjusting method greatly improves the accuracy of forecast results. Furthermore, the physical meanings of parameters in the numerical model are retained in adjusting results. From experimental evaluations, it is obvious that the root mean square error (RMSE) of each feature is reduced greatly. In detail, the average RMSE of 10m wind decreased by more than 45%, and the average RMSE of sea level pressure decreased by more than 50%. It is worth noting that the improvement here is the average of all forecasts for whole region within 7 days.

Machine Learning Applications for Head and Neck Imaging.

The head and neck (HN) consists of a large number of vital anatomic structures within a compact area. Imaging plays a central role in the diagnosis and management of major disorders affecting the HN. This article reviews the recent applications of machine learning (ML) in HN imaging with a focus on deep learning approaches. It categorizes ML applications in HN imaging into deep learning and traditional ML applications and provides examples of each category. It also discusses the main challenges facing the successful deployment of ML-based applications in the clinical setting and provides suggestions for addressing these challenges.

Machine learning applications in radiation oncology: Current use and needs to support clinical implementation.

Background and purposeThe use of artificial intelligence (AI)/ machine learning (ML) applications in radiation oncology is emerging, however no clear guidelines on commissioning of ML-based applications exist. The purpose of this study was therefore to investigate the current use and needs to support implementation of ML-based applications in routine clinical practice.Materials and methodsA survey was conducted among medical physicists in radiation oncology, consisting of four parts: clinical applications (1), model training, acceptance and commissioning (2), quality assurance (QA) in clinical practice and General Data Protection Regulation (GDPR) (3), and need for education and guidelines (4). Survey answers of medical physicists of the same radiation oncology centre were treated as a separate unique responder in case reporting on different AI applications.ResultsIn total, 213 medical physicists from 202 radiation oncology centres were included in the analysis. Sixty-nine percent (1 4 7) was using (37%) or preparing (32%) to use ML in clinic, mostly for contouring and treatment planning. In 86%, human observers were still involved in daily clinical use for quality check of the output of the ML algorithm. Knowledge on ethics, legislation and data sharing was limited and scattered among responders. Besides the need for (implementation) guidelines, training of medical physicists and larger databases containing multicentre data was found to be the top priority to accommodate the further introduction of ML in clinical practice.ConclusionThe results of this survey indicated the need for education and guidelines on the implementation and quality assurance of ML-based applications to benefit clinical introduction.

An overview of ML-based applications for next generation optical networks

Over the past few decades, the demand for the capacity and reliability of optical networks has continued to grow. In the meantime, optical networks with larger knowledge scales have become sources of numerous heterogeneous data. In order to handle these new challenges, many issues need to be resolved, among which the low-margin optical networks design, power optimization, routing and wavelength assignment (RWA), failure management are quite important. However, the use of traditional algorithms in the above four applications shows some shortcomings. Fortunately, artificial intelligence (AI), especially machine learning (ML), is regarded as one of the most promising methods to overcome these shortcomings. In this study, we review the applications of ML methods in solving these four issues. Although many ML-based researches have emerged, the applications of ML techniques in optical networks still face challenges. Therefore, we also discuss some possible future directions of investigating ML-based approaches in optical networks.

A Flexible Machine-Learning-Aware Architecture for Future WLANs

Lots of hopes have been placed on machine learning (ML) as a key enabler of future wireless networks. By taking advantage of large volumes of data, ML is expected to deal with the ever-increasing complexity of networking problems. Unfortunately, current networks are not yet prepared to support the ensuing requirements of ML-based applications in terms of data collection, processing, and output distribution. This article points out the architectural requirements that are needed to pervasively include ML as part of future wireless networks operation. Specifically, we look into wireless local area networks (WLANs), which, due to their nature, can be found in multiple forms, ranging from cloud-based to edge-computing-like deployments. In particular, we propose to adopt the International Telecommunication Union (ITU) unified architecture for 5G and beyond. Based on ITU's architecture, we provide insights on the main requirements and the major challenges of introducing ML to the multiple modalities of WLANs. Finally, we showcase the superiority of the architecture through an ML-enabled use case for future networks.

Analytics Solution Helps Identify Rod-Pump Failure at the Wellhead

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 192513, “Industrial Internet of Things Edge Analytics: Deploying Machine Learning at the Wellhead To Identify Rod-Pump Failure,” by Bartosz Boguslawski, Matthieu Boujonnier, Loryne Bissuel-Beauvais, Fahd Saghir, SPE, and Rajesh D. Sharma, SPE, Schneider Electric, prepared for the 2018 SPE Middle East Artificial Lift Conference and Exhibition, Manama, Bahrain, 28–29 November. The paper has not been peer reviewed. Industrial-Internet-of-things (IIOT) architecture provides an opportunity to improve asset uptime and maintenance of assets, reduce safety risks, and optimize operational costs. However, to turn data into meaningful insights, the industry must make full benefit of machine-learning (ML) models. This paper presents an analytics solution for identifying rod-pump failure capable of automated dynacard recognition at the wellhead that uses an ensemble of ML models. The proposed solution does not require Internet connectivity to generate alarms and meets confidentiality requirements. Rod-Pump-Control (RPC) Architecture Thanks to recent progress in microelectronics, the embedding of ML models in remote places with scarce connectivity, known as edge computing, is possible. Thanks to the insights generated at the oil field, onsite maintenance teams can apply immediate corrective responses, working efficiently and safely. RPC architecture enables automated control of sucker-rod pumps. A variable-speed drive (VSD) controls the pump by adjusting the speed of the motor to downhole conditions. The VSD is controlled by a remote terminal unit (RTU) that provides speed reference for improved rod-pump control. The RTU also collects sensor measurements. In the work covered in the paper, two sensors, namely the proximity sensor and the load cell, are of particular interest. The proximity sensor is mounted near the crank arm, while the load cell is a transducer mounted between the polished rod clamp and the bridle. The RTU sends the measurements to a touchscreen human/machine interface for a local user. It also communicates the measurements to a supervisory control and data-acquisition (SCADA) system. The communication between the RTU and the SCADA host is established through external wireless communication devices such as radios or cellular modems. Furthermore, in the ex-ample discussed in the paper, a local edge computing gateway is added to the architecture. Edge gateways have significant computing power and can run ML-based applications. The gateway retrieves the data from the RTU and performs onsite analytics, generates alarms, and communicates with the cloud sporadically. Using the measurements from the two sensors, the RPC produces a surface card (or dynacard), from which downhole pump conditions may be deduced, and consequently calculates a downhole card, from which downhole pump conditions may be inferred. The downhole card is, in essence, a translated surface card with oscillatory harmonics removed. An experienced operator is able to infer from the downhole card whether a pump is operating normally or has failed in some way.

Big Data and Machine Learning Driven Open5GMEC for Vehicular Communications

Mobile Edge Computing (MEC) is an emerging technology and an essential component of 5G networks to bring cloud services closer to users. That means data collection, storage, processing, computing, communication, and network control are implemented at network edges. MEC is expected to be able to satisfy a variety of delay-sensitive services and applications. On the other hand, the development of vehicles to everything (V2X) communication brings many requirements to future networks to guarantee full intelligence, automatic, and faster computation, management, and optimization to fulfill network QoS (quality of service) and QoE (quality of experience). To deal with those requirements, recently, software-defined networking (SDN), network functions virtualization (NFV), big data, and machine learning (ML) have been proposed as emerging technologies and the necessary tools for MEC and vehicular networks. This study aims to integrate those technologies to build a comprehensive architecture and an experimental framework for future 5G MEC called Open5GMEC. Moreover, the authors analyzed challenges and proposed relevant solutions for future vehicular communications in 5G networks. Finally, based on this framework, we successfully implemented several powerful ML-based applications for V2X such as object detection, network slicing, and migration services, which are executed at Broadband Mobile Lab (BML), National Chiao Tung University (NCTU).

Making machine learning useable

Despite the recognised value of machine learning ML techniques and high expectation of applying ML techniques within various applications, users often find it difficult to effectively apply ML techniques in practise because of complicated interfaces between ML algorithms and users. This paper focuses on investigating making ML useable from the point of view of how human-computer interaction HCI techniques benefit ML in order to simplify the interface between users and ML algorithms. We formulate possible research directions in making ML useable based on human factors, decision making and trust in ML. We strongly believe that a trustworthy decision making based on ML results, which is the ultimate goal of ML-based applications, contributes to the overall application performance and makes ML more useable. Two case studies of measurable decision making and revealing internal states of ML process are presented to show how HCI techniques are used to make ML useable.