A machine learning method to predict the technology adoption of blockchain in Palestinian firms

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ihab K.A. Hamdan ◽  
Wulamu Aziguli ◽  
Dezheng Zhang ◽  
Eli Sumarliah ◽  
Fauziyah Fauziyah
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Business Leaders ◽  
Ease Of Use ◽  
Machine Learning Algorithms ◽  
Content Type ◽  
Industrial Sectors ◽  
Technological Adoption ◽  
Perceived Benefit ◽  
Machine Learning Approach

PurposeThe study aims to deliver a decision support system for business leaders to estimate the potential for effective technological adoption of the blockchain (TAB) with a machine learning approach.Design/methodology/approachThis study uses a Bayesian network examination to develop an extrapolative system of decision support, highlighting the influential determinants that managers can employ to predict the TAB possibilities in their companies. Data were gathered from 167 SMEs in the largest industrial sectors in Palestine.FindingsThe results reveal perceived benefit and ease of use as the most influential determinants of the TAB.Originality/valueThis research is an initial effort to examine factors influencing TAB in the perspective of SMEs in Palestine using machine learning algorithms.

Development of a machine-learning-based decision support mechanism for predicting chemical tanker cleaning activity

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Burak Cankaya ◽  
Berna Eren Tokgoz ◽  
Ali Dag ◽  
K.C. Santosh
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Test Data ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Comparative Approach ◽  
Support Vector ◽  
Data Set ◽  
Content Type ◽  
Vehicle Activity

Purpose This paper aims to propose a machine learning-based automatic labeling methodology for chemical tanker activities that can be applied to any port with any number of active tankers and the identification of important predictors. The methodology can be applied to any type of activity tracking that is based on automatically generated geospatial data. Design/methodology/approach The proposed methodology uses three machine learning algorithms (artificial neural networks, support vector machines (SVMs) and random forest) along with information fusion (IF)-based sensitivity analysis to classify chemical tanker activities. The data set is split into training and test data based on vessels, with two vessels in the training data and one in the test data set. Important predictors were identified using a receiver operating characteristic comparative approach, and overall variable importance was calculated using IF from the top models. Findings Results show that an SVM model has the best balance between sensitivity and specificity, at 93.5% and 91.4%, respectively. Speed, acceleration and change in the course on the ground for the vessels are identified as the most important predictors for classifying vessel activity. Research limitations/implications The study evaluates the vessel movements waiting between different terminals in the same port, but not their movements between different ports for their tank-cleaning activities. Practical implications The findings in this study can be used by port authorities, shipping companies, vessel operators and other stakeholders for decision support, performance tracking, as well as for automated alerts. Originality/value This analysis makes original contributions to the existing literature by defining and demonstrating a methodology that can automatically label vehicle activity based on location data and identify certain characteristics of the activity by finding important location-based predictors that effectively classify the activity status.

scholarly journals A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium

2021 ◽  
Vol 9 (5) ◽  
pp. 1034
Author(s):  
Carlos Sabater ◽  
Lorena Ruiz ◽  
Abelardo Margolles
Keyword(s):  
Machine Learning ◽  
Supervised Classification ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Human Milk Oligosaccharides ◽  
Future Studies ◽  
High Fiber ◽  
Machine Learning Approach ◽  
Prebiotic Oligosaccharides

This study aimed to recover metagenome-assembled genomes (MAGs) from human fecal samples to characterize the glycosidase profiles of Bifidobacterium species exposed to different prebiotic oligosaccharides (galacto-oligosaccharides, fructo-oligosaccharides and human milk oligosaccharides, HMOs) as well as high-fiber diets. A total of 1806 MAGs were recovered from 487 infant and adult metagenomes. Unsupervised and supervised classification of glycosidases codified in MAGs using machine-learning algorithms allowed establishing characteristic hydrolytic profiles for B. adolescentis, B. bifidum, B. breve, B. longum and B. pseudocatenulatum, yielding classification rates above 90%. Glycosidase families GH5 44, GH32, and GH110 were characteristic of B. bifidum. The presence or absence of GH1, GH2, GH5 and GH20 was characteristic of B. adolescentis, B. breve and B. pseudocatenulatum, while families GH1 and GH30 were relevant in MAGs from B. longum. These characteristic profiles allowed discriminating bifidobacteria regardless of prebiotic exposure. Correlation analysis of glycosidase activities suggests strong associations between glycosidase families comprising HMOs-degrading enzymes, which are often found in MAGs from the same species. Mathematical models here proposed may contribute to a better understanding of the carbohydrate metabolism of some common bifidobacteria species and could be extrapolated to other microorganisms of interest in future studies.

scholarly journals A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients

2021 ◽  
Vol 27 ◽  
pp. 107602962199118
Author(s):  
Logan Ryan ◽  
Samson Mataraso ◽  
Anna Siefkas ◽  
Emily Pellegrini ◽  
Gina Barnes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Venous Thrombosis ◽  
Deep Venous Thrombosis ◽  
Primary Outcome ◽  
Scoring Systems ◽  
Hospitalized Patients ◽  
Machine Learning Algorithms ◽  
Cancer History ◽  
Machine Learning Approach

Deep venous thrombosis (DVT) is associated with significant morbidity, mortality, and increased healthcare costs. Standard scoring systems for DVT risk stratification often provide insufficient stratification of hospitalized patients and are unable to accurately predict which inpatients are most likely to present with DVT. There is a continued need for tools which can predict DVT in hospitalized patients. We performed a retrospective study on a database collected from a large academic hospital, comprised of 99,237 total general ward or ICU patients, 2,378 of whom experienced a DVT during their hospital stay. Gradient boosted machine learning algorithms were developed to predict a patient’s risk of developing DVT at 12- and 24-hour windows prior to onset. The primary outcome of interest was diagnosis of in-hospital DVT. The machine learning predictors obtained AUROCs of 0.83 and 0.85 for DVT risk prediction on hospitalized patients at 12- and 24-hour windows, respectively. At both 12 and 24 hours before DVT onset, the most important features for prediction of DVT were cancer history, VTE history, and internal normalized ratio (INR). Improved risk stratification may prevent unnecessary invasive testing in patients for whom DVT cannot be ruled out using existing methods. Improved risk stratification may also allow for more targeted use of prophylactic anticoagulants, as well as earlier diagnosis and treatment, preventing the development of pulmonary emboli and other sequelae of DVT.

scholarly journals AI for augmenting human judgement in Business Processes Management

2021 ◽  
Vol 201 (3) ◽  
pp. 507-518
Author(s):  
Łukasz Osuszek ◽  
Stanisław Stanek
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Business Processes ◽  
Workflow Management ◽  
Organizational Memory ◽  
Machine Learning Algorithms ◽  
Analytical Models ◽  
Case Based Reasoning ◽  
Historical Case

The paper outlines the recent trends in the evolution of Business Process Management (BPM) – especially the application of AI for decision support. AI has great potential to augment human judgement. Indeed, Machine Learning might be considered as a supplementary and complimentary solution to enhance and support human productivity throughout all aspects of personal and professional life. The idea of merging technologies for organizational learning and workflow management was first put forward by Wargitsch. Herein, completed business cases stored in an organizational memory are used to configure new workflows, while the selection of an appropriate historical case is supported by a case-based reasoning component. This informational environment has been recognized in the world as being effective and has become quite common because of the significant increase in the use of artificial intelligence tools. This article discusses also how automated planning techniques (one of the oldest areas in AI) can be used to enable a new level of automation and processing support. The authors of the article decided to analyse this topic and discuss the scientific state of the art and the application of AI in BPM systems for decision-making support. It should be noted that readily available software exists for the needs of the development of such systems in the field of artificial intelligence. The paper also includes a unique case study with production system of Decision Support, using controlled machine learning algorithms to predictive analytical models.

Using Supervised Machine Learning Algorithms for Automated Lithology Prediction from Wireline Log Data

2021 ◽  
Author(s):  
Marian Popescu ◽  
Rebecca Head ◽  
Tim Ferriday ◽  
Kate Evans ◽  
Jose Montero ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Depth Interval ◽  
Log Data ◽  
Machine Learning Approach ◽  
Lithology Prediction ◽  
Logging While Drilling

Abstract This paper presents advancements in machine learning and cloud deployment that enable rapid and accurate automated lithology interpretation. A supervised machine learning technique is described that enables rapid, consistent, and accurate lithology prediction alongside quantitative uncertainty from large wireline or logging-while-drilling (LWD) datasets. To leverage supervised machine learning, a team of geoscientists and petrophysicists made detailed lithology interpretations of wells to generate a comprehensive training dataset. Lithology interpretations were based on applying determinist cross-plotting by utilizing and combining various raw logs. This training dataset was used to develop a model and test a machine learning pipeline. The pipeline was applied to a dataset previously unseen by the algorithm, to predict lithology. A quality checking process was performed by a petrophysicist to validate new predictions delivered by the pipeline against human interpretations. Confidence in the interpretations was assessed in two ways. The prior probability was calculated, a measure of confidence in the input data being recognized by the model. Posterior probability was calculated, which quantifies the likelihood that a specified depth interval comprises a given lithology. The supervised machine learning algorithm ensured that the wells were interpreted consistently by removing interpreter biases and inconsistencies. The scalability of cloud computing enabled a large log dataset to be interpreted rapidly; >100 wells were interpreted consistently in five minutes, yielding >70% lithological match to the human petrophysical interpretation. Supervised machine learning methods have strong potential for classifying lithology from log data because: 1) they can automatically define complex, non-parametric, multi-variate relationships across several input logs; and 2) they allow classifications to be quantified confidently. Furthermore, this approach captured the knowledge and nuances of an interpreter's decisions by training the algorithm using human-interpreted labels. In the hydrocarbon industry, the quantity of generated data is predicted to increase by >300% between 2018 and 2023 (IDC, Worldwide Global DataSphere Forecast, 2019–2023). Additionally, the industry holds vast legacy data. This supervised machine learning approach can unlock the potential of some of these datasets by providing consistent lithology interpretations rapidly, allowing resources to be used more effectively.

The role of personalization on continuance intention in food service mobile apps

2019 ◽  
Vol 31 (2) ◽  
pp. 734-752 ◽  
Author(s):  
Jee-Won Kang ◽  
Young Namkung
Keyword(s):  
Perceived Risk ◽  
Mobile Apps ◽  
Perceived Value ◽  
Food Service ◽  
Ease Of Use ◽  
Perceived Ease Of Use ◽  
Content Type ◽  
Perceived Benefit ◽  
Privacy Calculus

PurposeThis paper aims to examine consumers’ behaviors toward personalized services offered by branded mobile apps in the food service industry by applying privacy calculus theory and technology acceptance model (TAM). Further, this research identified the moderating role of technology anxiety.Design/methodology/approachAn online survey was carried out to investigate the role of personalization on continuance intention toward branded mobile apps. In total, 348 valid responses were analyzed to test hypotheses using structural equation modeling.FindingsThe results show that personalization had significantly affected perceived benefit, perceived risk and perceived ease of use. Perceived benefit had positive effects on perceived value of disclosure, but perceived risk did not affect perceived value of disclosure. Perceived value of disclosure and perceived ease of use were linked to trust. Trust, in turn, positively affected intentions to use mobile apps. With regard to the moderating effect of technology anxiety, it had a significant moderating impact on the relationship between personalization and perceived risk. However, it did not moderate the relationship between personalization and perceived benefit.Practical implicationsThe findings of this study could provide useful theoretical and practical implications related to the successful implementation of mobile marketing.Originality/valueThis study proposes the integrated model of privacy calculus theory and the TAM for deeper understanding of the customers’ responses toward personalization of branded mobile apps.

scholarly journals Employing a Machine Learning Approach to Detect Combined Internet of Things Attacks against Two Objective Functions Using a Novel Dataset

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
John Foley ◽  
Naghmeh Moradpoor ◽  
Henry Ochenyi
Keyword(s):  
Machine Learning ◽  
Low Power ◽  
Vulnerability Analysis ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Lossy Networks ◽  
Global Networks ◽  
Machine Learning Approach ◽  
Iot Devices ◽  
Network Metrics

One of the important features of routing protocol for low-power and lossy networks (RPLs) is objective function (OF). OF influences an IoT network in terms of routing strategies and network topology. On the contrary, detecting a combination of attacks against OFs is a cutting-edge technology that will become a necessity as next generation low-power wireless networks continue to be exploited as they grow rapidly. However, current literature lacks study on vulnerability analysis of OFs particularly in terms of combined attacks. Furthermore, machine learning is a promising solution for the global networks of IoT devices in terms of analysing their ever-growing generated data and predicting cyberattacks against such devices. Therefore, in this paper, we study the vulnerability analysis of two popular OFs of RPL to detect combined attacks against them using machine learning algorithms through different simulated scenarios. For this, we created a novel IoT dataset based on power and network metrics, which is deployed as part of an RPL IDS/IPS solution to enhance information security. Addressing the captured results, our machine learning approach is successful in detecting combined attacks against two popular OFs of RPL based on the power and network metrics in which MLP and RF algorithms are the most successful classifier deployment for single and ensemble models.

Machine learning-based anomaly detection via integration of manufacturing, inspection and after-sales service data

2017 ◽  
Vol 117 (5) ◽  
pp. 927-945 ◽  
Author(s):  
Taehoon Ko ◽  
Je Hyuk Lee ◽  
Hyunchang Cho ◽  
Sungzoon Cho ◽  
Wounjoo Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Management ◽  
Data Integration ◽  
Anomaly Detection ◽  
Learning Algorithms ◽  
Perceived Quality ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Content Type ◽  
Service Data

Purpose Quality management of products is an important part of manufacturing process. One way to manage and assure product quality is to use machine learning algorithms based on relationship among various process steps. The purpose of this paper is to integrate manufacturing, inspection and after-sales service data to make full use of machine learning algorithms for estimating the products’ quality in a supervised fashion. Proposed frameworks and methods are applied to actual data associated with heavy machinery engines. Design/methodology/approach By following Lenzerini’s formula, manufacturing, inspection and after-sales service data from various sources are integrated. The after-sales service data are used to label each engine as normal or abnormal. In this study, one-class classification algorithms are used due to class imbalance problem. To address multi-dimensionality of time series data, the symbolic aggregate approximation algorithm is used for data segmentation. Then, binary genetic algorithm-based wrapper approach is applied to segmented data to find the optimal feature subset. Findings By employing machine learning-based anomaly detection models, an anomaly score for each engine is calculated. Experimental results show that the proposed method can detect defective engines with a high probability before they are shipped. Originality/value Through data integration, the actual customer-perceived quality from after-sales service is linked to data from manufacturing and inspection process. In terms of business application, data integration and machine learning-based anomaly detection can help manufacturers establish quality management policies that reflect the actual customer-perceived quality by predicting defective engines.

A machine learning approach to achieve accurate time series forecast of sea-wave conditions

2020 ◽  
Author(s):  
Giulia Cremonini ◽  
Giovanni Besio ◽  
Daniele Lagomarsino ◽  
Agnese Seminara
Keyword(s):  
Machine Learning ◽  
Environmental Variables ◽  
Machine Learning Algorithms ◽  
Accurate Estimation ◽  
Time Series Forecast ◽  
Machine Learning Approach ◽  
Physically Based ◽  
Physically Based Models ◽  
Wave Conditions ◽  
Sea Wave

<p>Reliable forecast of environmental variables is fundamental in managing<br>risk associated with hazard scenarios. In this work, we use state of the<br>art machine learning algorithms to build forecasting models and to get<br>accurate estimation of sea wave conditions. We exploit multivariate time<br>series of environmental variables, extracted either from hindcast<br>database (provided by MeteOcean Group at DICCA) or observed data from<br>sparse buoys. In this way, future values of sea wave height can be<br>predicted in order to evaluate the risk associated with incoming<br>scenarios. The aim is to provide new forecasting tools representing an<br>alternative to physically based models which have higher computational<br>cost.</p>

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