In recent decades, the number of cases of knee arthroplasty among people of working age has increased. The integrated clinical pathway ‘back at work after surgery’ is an initiative to reduce the possible cost of sick leave. The evaluation of this pathway, like many clinical studies, faces the challenge of small data sets with a relatively high number of features. In this study, we investigate the possibility of identifying features that are important in determining the duration of rehabilitation, expressed in the return-to-work period, by using feature selection tools. Several models are used to classify the patient’s data into two classes, and the results are evaluated based on the accuracy and the quality of the ordering of the features, for which we introduce a ranking score. A selection of estimators are used in an optimization step, reorganizing the feature ranking. The results show that for some models, the proposed optimization results in a better ordering of the features. The ordering of the features is evaluated visually and identified by the ranking score. Furthermore, for all models, higher accuracy, with a maximum of 91%, is achieved by applying the optimization process. The features that are identified as relevant for the duration of the return-to-work period are discussed and provide input for further research.
DOCUMENT
The security of online assessments is a major concern due to widespread cheating. One common form of cheating is impersonation, where students invite unauthorized persons to take assessments on their behalf. Several techniques exist to handle impersonation. Some researchers recommend use of integrity policy, but communicating the policy effectively to the students is a challenge. Others propose authentication methods like, password and fingerprint; they offer initial authentication but are vulnerable thereafter. Face recognition offers post-login authentication but necessitates additional hardware. Keystroke Dynamics (KD) has been used to provide post-login authentication without any additional hardware, but its use is limited to subjective assessment. In this work, we address impersonation in assessments with Multiple Choice Questions (MCQ). Our approach combines two key strategies: reinforcement of integrity policy for prevention, and keystroke-based random authentication for detection of impersonation. To the best of our knowledge, it is the first attempt to use keystroke dynamics for post-login authentication in the context of MCQ. We improve an online quiz tool for the data collection suited to our needs and use feature engineering to address the challenge of high-dimensional keystroke datasets. Using machine learning classifiers, we identify the best-performing model for authenticating the students. The results indicate that the highest accuracy (83%) is achieved by the Isolation Forest classifier. Furthermore, to validate the results, the approach is applied to Carnegie Mellon University (CMU) benchmark dataset, thereby achieving an improved accuracy of 94%. Though we also used mouse dynamics for authentication, but its subpar performance leads us to not consider it for our approach.
DOCUMENT
Reducing the use of pesticides by early visual detection of diseases in precision agriculture is important. Because of the color similarity between potato-plant diseases, narrow band hyper-spectral imaging is required. Payload constraints on unmanned aerial vehicles require reduc- tion of spectral bands. Therefore, we present a methodology for per-patch classification combined with hyper-spectral band selection. In controlled experiments performed on a set of individual leaves, we measure the performance of five classifiers and three dimensionality-reduction methods with three patch sizes. With the best-performing classifier an error rate of 1.5% is achieved for distinguishing two important potato-plant diseases.
MULTIFILE
The “Age-Friendly Cities & Communities: States of the Art and Future Perspectives”publication presents contemporary, innovative, and insightful narratives, debates, and frameworks based on an international collection of papers from scholars spanning the fields of gerontology, social sciences, architecture, computer science, and gerontechnology. This extensive collection of papers aims to move the narrative and debates forward in this interdisciplinary field of age-friendly cities and communities. CC BY-NC-ND Book CC BY Chapters © 2021 by the authors Original book at: https://doi.org/10.3390/books978-3-0365-1226-6 (This book is a printed edition of the Special Issue Feature Papers "Age-Friendly Cities & Communities: State of the Art and Future Perspectives" that was published in IJERPH)
MULTIFILE
Abstract Background: COVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities. Objective: We aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques. Methods: We collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands. Results: The final prediction model had an R2 of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters <10 μm (PM10) in the air, the percentage of Labour party voters, and the number of children in a household. Conclusions: Data about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared.
LINK
Understanding how horses interact with landscapes is key to designing environments that support welfare and biodiversity. Yet, little is known about how domestic horses use specific elements within a landscape. This study examined the behavioral interactions of seven Swedish warmblood mares (1–3 years old) with naturally occurring landscape features in the Kumlan Nature Reserve (Sweden) during July and August 2021. HoofStep® sensors continuously recorded equine behaviors, categorized as highly active, active, resting, or eating. Manly Selection Ratios (MSRs) were used to assess landscape feature selection (LFS) relative to availability. A generalized linear model (Gamma distribution, log link) tested the effects of horse, behavior, landscape feature, time of day, temperature, rainfall, and month on LFS. Significant main effects included horse, landscape feature, month, rainfall, and temperature (p < .001). Two-way interactions showed that behavior was linked to LFS and that selection was influenced by weather. For instance, tree rows and hedges were preferred during rainfall (Exp(B) = 1.17, p = 0.01), but avoided as temperatures rose (Exp(B) = -0.51, p < 0.001). Three-way interactions highlighted individual preferences, i.e., Horse A preferred resting on a sandbank (Exp(B) = 42.48, p < 0.05), and Horse B in a blackberry patch (Exp(B) = 25.22, p < 0.05). Horse C was active on a sandbank with vegetation (Exp(B) = 22.57, p = 0.01), while Horse D preferred the pool (Exp(B) = 90.44, p < 0.001). Findings suggest that both landscape and weather shape equine behavior, with notable individual variation. Landscape design should incorporate diverse features to meet the behavioral needs of individual horses and to support welfare and biodiversity goals.
LINK
This research paper looks at a selection of science-fiction films and its connection with the progression of the use of television, telephone and print media. It also analyzes statistical data obtained from a questionnaire conducted by the research group regarding the use of communication media.
DOCUMENT
![Does Science-Fiction predict [or change] the future?](https://publinova-harvester-content-prod.s3.amazonaws.com/thumbnails/files/previews/pdf/20250506145555255024.adbf7edd-9619-4d0e-9a17-e0e7096adc7f-thumbnail-400x300.png)
Within recent years, Financial Credit Risk Assessment (FCRA) has become an increasingly important issue within the financial industry. Therefore, the search for features that can predict the credit risk of an organization has increased. Using multiple statistical techniques, a variance of features has been proposed. Applying a structured literature review, 258 papers have been selected. From the selected papers, 835 features have been identified. The features have been analyzed with respect to the type of feature, the information sources needed and the type of organization that applies the features. Based on the results of the analysis, the features have been plotted in the FCRA Model. The results show that most features focus on hard information from a transactional source, based on official information with a high latency. In this paper, we readdress and -present our earlier work [1]. We extended the previous research with more detailed descriptions of the related literature, findings, and results, which provides a grounded basis from which further research on FCRA can be conducted.
DOCUMENT
Ambient activity monitoring systems produce large amounts of data, which can be used for health monitoring. The problem is that patterns in this data reflecting health status are not identified yet. In this paper the possibility is explored of predicting the functional health status (the motor score of AMPS = Assessment of Motor and Process Skills) of a person from data of binary ambient sensors. Data is collected of five independently living elderly people. Based on expert knowledge, features are extracted from the sensor data and several subsets are selected. We use standard linear regression and Gaussian processes for mapping the features to the functional status and predict the status of a test person using a leave-oneperson-out cross validation. The results show that Gaussian processes perform better than the linear regression model, and that both models perform better with the basic feature set than with location or transition based features. Some suggestions are provided for better feature extraction and selection for the purpose of health monitoring. These results indicate that automated functional health assessment is possible, but some challenges lie ahead. The most important challenge is eliciting expert knowledge and translating that into quantifiable features.
DOCUMENT
The huge number of images shared on the Web makes effective cataloguing methods for efficient storage and retrieval procedures specifically tailored on the end-user needs a very demanding and crucial issue. In this paper, we investigate the applicability of Automatic Image Annotation (AIA) for image tagging with a focus on the needs of database expansion for a news broadcasting company. First, we determine the feasibility of using AIA in such a context with the aim of minimizing an extensive retraining whenever a new tag needs to be incorporated in the tag set population. Then, an image annotation tool integrating a Convolutional Neural Network model (AlexNet) for feature extraction and a K-Nearest-Neighbours classifier for tag assignment to images is introduced and tested. The obtained performances are very promising addressing the proposed approach as valuable to tackle the problem of image tagging in the framework of a broadcasting company, whilst not yet optimal for integration in the business process.
DOCUMENT
