Search results for: nursing interventions classification

Authors: K. P. Sandesh, M. H. Suman

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Text processing plays an important role in information retrieval, data-mining, and web search. Measuring the similarity between the documents is an important operation in the text processing field. In this project, a new similarity measure is proposed. To compute the similarity between two documents with respect to a feature the proposed measure takes the following three cases into account: (1) The feature appears in both documents; (2) The feature appears in only one document and; (3) The feature appears in none of the documents. The proposed measure is extended to gauge the similarity between two sets of documents. The effectiveness of our measure is evaluated on several real-world data sets for text classification and clustering problems, especially in banking and health sectors. The results show that the performance obtained by the proposed measure is better than that achieved by the other measures.

Keywords: document classification, document clustering, entropy, accuracy, classifiers, clustering algorithms

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Authors: Artitaya Sabangbal, Darawan Augsornwan, Palakorn Surakunprapha, Lalida Petphai

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Background: Srinagarind Hospital Ward 3C has about 180 cases of patients with head and neck cancer per year. Almost all of these patients suffer with pain, fatigue, low self image, swallowing problem and when the tumor is larger they will have breathing problem. Many of them have complication after operation such as pressure sore, pneumonia, deep vein thrombosis. Nursing activity is very important to prevent the complication especially promoting patients early ambulation. The objective of this study was to develop early ambulation protocol for patients with head and neck cancer undergoing operation. Method: this study is one part of nursing system development in patients undergoing operation in Ward 3C. It is a participation action research divided into 3 phases Phase 1 Situation review: In this phase we review the clinical outcomes, process of care, from document such as nurses note and interview nurses, patients and family about early ambulation. Phase 2 Searching nursing intervention about early ambulation from previous study then establish protocol . This phase we have picture package of early ambulation. Phase 3 implementation and evaluation. Result: Patients with head and neck cancer after operation can follow early ambulation protocol 100%, 85 % of patients can follow protocol within 2 days after operation and 100% can follow protocol within 3 days. No complications occur. Patients satisfaction in very good level is 58% and in good level is 42% Length of hospital stay is 6 days in patients with wide excision and 16 day in patients with flap coverage. Conclusion: The early ambulation protocol is appropriate for patients with head and neck cancer who undergo operation. This can restore physical health, reduce complication and increase patients satisfaction.

Keywords: nursing system, early ambulation, head and neck cancer, operation

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Authors: Liane Marcia Freitas Silva, Fernando Augusto Silva Marins, Maria Silene Alexandre Leite

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The adoption of a network structure, like in the supply chains, favors the increase of dependence between companies and, by consequence, their vulnerability. Environment disasters, sociopolitical and economical events, and the dynamics of supply chains elevate the uncertainty of their operation, favoring the occurrence of events that can generate break up in the operations and other undesired consequences. Thus, supply chains are exposed to various risks that can influence the profitability of companies involved, and there are several previous studies that have proposed risk classification models in order to categorize the risks and to manage them. The objective of this paper is to analyze and discuss thirty of these risk classification models by means a theoretical survey. The research method adopted for analyzing and discussion includes three phases: The identification of the types of risks proposed in each one of the thirty models, the grouping of them considering equivalent concepts associated to their definitions, and, the analysis of these risks groups, evaluating their similarities and differences. After these analyses, it was possible to conclude that, in fact, there is more than thirty risks types identified in the literature of Supply Chains, but some of them are identical despite of be used distinct terms to characterize them, because different criteria for risk classification are adopted by researchers. In short, it is observed that some types of risks are identified as risk source for supply chains, such as, demand risk, environmental risk and safety risk. On the other hand, other types of risks are identified by the consequences that they can generate for the supply chains, such as, the reputation risk, the asset depreciation risk and the competitive risk. These results are consequence of the disagreements between researchers on risk classification, mainly about what is risk event and about what is the consequence of risk occurrence. An additional study is in developing in order to clarify how the risks can be generated, and which are the characteristics of the components in a Supply Chain that leads to occurrence of risk.

Keywords: sisks classification, survey, supply chain management, theoretical discussion

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Authors: Mehran Asadi, Afrand Agah

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In data mining, partitioning is used as a fundamental tool for classification. With the help of partitioning, we study the structure of data, which allows us to envision decision rules, which can be applied to classification trees. In this research, we used online social network dataset and all of its attributes (e.g., Node features, labels, etc.) to determine what constitutes an above average chance of being a divergent node. We used the R statistical computing language to conduct the analyses in this report. The data were found on the UC Irvine Machine Learning Repository. This research introduces the basic concepts of classification in online social networks. In this work, we utilize overfitting and describe different approaches for evaluation and performance comparison of different classification methods. In classification, the main objective is to categorize different items and assign them into different groups based on their properties and similarities. In data mining, recursive partitioning is being utilized to probe the structure of a data set, which allow us to envision decision rules and apply them to classify data into several groups. Estimating densities is hard, especially in high dimensions, with limited data. Of course, we do not know the densities, but we could estimate them using classical techniques. First, we calculated the correlation matrix of the dataset to see if any predictors are highly correlated with one another. By calculating the correlation coefficients for the predictor variables, we see that density is strongly correlated with transitivity. We initialized a data frame to easily compare the quality of the result classification methods and utilized decision trees (with k-fold cross validation to prune the tree). The method performed on this dataset is decision trees. Decision tree is a non-parametric classification method, which uses a set of rules to predict that each observation belongs to the most commonly occurring class label of the training data. Our method aggregates many decision trees to create an optimized model that is not susceptible to overfitting. When using a decision tree, however, it is important to use cross-validation to prune the tree in order to narrow it down to the most important variables.

Keywords: online social networks, data mining, social cloud computing, interaction and collaboration

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Authors: Subham Kharel, Sudha Ravindranath, A. Vidya, B. Chandrasekaran, K. Ganesha Raj, T. Shesadri

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Digitization of urban features is a tedious and time-consuming process when done manually. In addition to this problem, Indian cities have complex habitat patterns and convoluted clustering patterns, which make it even more difficult to map features. This paper makes an attempt to classify urban objects in the satellite image using object-oriented classification techniques in which various classes such as vegetation, water bodies, buildings, and shadows adjacent to the buildings were mapped semi-automatically. Building layer obtained as a result of object-oriented classification along with already available building layers was used. The main focus, however, lay in the extraction of high-rise buildings using spatial technology, digital image processing, and modeling, which would otherwise be a very difficult task to carry out manually. Results indicated a considerable rise in the total number of buildings in the city. High-rise buildings were successfully mapped using satellite imagery, spatial technology along with logical reasoning and mathematical considerations. The results clearly depict the ability of Remote Sensing and GIS to solve complex problems in urban scenarios like studying urban sprawl and identification of more complex features in an urban area like high-rise buildings and multi-dwelling units. Object-Oriented Technique has been proven to be effective and has yielded an overall efficiency of 80 percent in the classification of high-rise buildings.

Keywords: object oriented classification, shadow extraction, high-rise buildings, satellite imagery, spatial technology

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Authors: Keden Montgomery, Kathie Ardzejewska, Alison Casey, Rosemarie Hogan

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Research was undertaken to explore the question “what is known about the experiences of regional, rural and remote Australian women undertaking a Bachelor of Nursing program delivered online?”. The findings will support future research aimed at improving the retention and completion rates of women studying nursing in regional, rural and remote areas.  There is a critical shortage of nurses working in regional, rural and remote (RRR) Australia. It is well supported that this shortage of nurses is most likely to be addressed by nursing students who are completing their studies in RRR areas. Despite this, students from RRR Australia remain an equity group and experience poorer outcomes than their metropolitan counterparts. Completion rates for RRR students who enrol in tertiary education courses are much less than students from metropolitan areas. In addition to this, RRR students are less likely than students from metropolitan areas to gain a tertiary level qualification at all, and even less likely to gain a Bachelor level degree which is required for Registered Nurses. Supporting students to remain in regional, rural and remote areas while they study reduces the need for students to relocate to metropolitan areas and to continue living and working in RRR areas after graduation. This research holds implications for workforce shortages internationally.

Keywords: nurse education, online education, regional, rural, remote, workforce

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Authors: Kumar Shashvat, Amol P. Bhondekar

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In the midst of the five senses, odor is the most reminiscent and least understood. Odor testing has been mysterious and odor data fabled to most practitioners. The delinquent of recognition and classification of odor is important to achieve. The facility to smell and predict whether the artifact is of further use or it has become undesirable for consumption; the imitation of this problem hooked on a model is of consideration. The general industrial standard for this classification is color based anyhow; odor can be improved classifier than color based classification and if incorporated in machine will be awfully constructive. For cataloging of odor for peas, trees and cashews various discriminative approaches have been used Discriminative approaches offer good prognostic performance and have been widely used in many applications but are incapable to make effectual use of the unlabeled information. In such scenarios, generative approaches have better applicability, as they are able to knob glitches, such as in set-ups where variability in the series of possible input vectors is enormous. Generative models are integrated in machine learning for either modeling data directly or as a transitional step to form an indeterminate probability density function. The algorithms or models Linear Discriminant Analysis and Naive Bayes Classifier have been used for classification of the odor of cashews. Linear Discriminant Analysis is a method used in data classification, pattern recognition, and machine learning to discover a linear combination of features that typifies or divides two or more classes of objects or procedures. The Naive Bayes algorithm is a classification approach base on Bayes rule and a set of qualified independence theory. Naive Bayes classifiers are highly scalable, requiring a number of restraints linear in the number of variables (features/predictors) in a learning predicament. The main recompenses of using the generative models are generally a Generative Models make stronger assumptions about the data, specifically, about the distribution of predictors given the response variables. The Electronic instrument which is used for artificial odor sensing and classification is an electronic nose. This device is designed to imitate the anthropological sense of odor by providing an analysis of individual chemicals or chemical mixtures. The experimental results have been evaluated in the form of the performance measures i.e. are accuracy, precision and recall. The investigational results have proven that the overall performance of the Linear Discriminant Analysis was better in assessment to the Naive Bayes Classifier on cashew dataset.

Keywords: odor classification, generative models, naive bayes, linear discriminant analysis

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Authors: Edwina Owusu Panin, Kwame Baah Owusu Panin

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This abstract intends to demonstrate multiple strategies of autism management in Ghana by exploring the possibilities. The advantages of adopting creative art forms as a therapeutic method. Autism is a developmental disorder that includes social interaction, communication, and repetitive behaviours. In Ghana, as in many other countries, there is a rising demand for effective intervention and support for people with autism and their families. Creative arts such as music, dance, drama and visual arts have shown promise in promoting communication, social interaction and inclusion of people with autism. These art forms provide alternative channels for self-expression and can be powerful tools for autistic people to interact with the world, their friends and families around them. Creative art forms interventions have been found to improve social skills, improve emotion regulation, promote creativity and increase self-confidence in people with autism. This study examines existing programs and interventions in Ghana involving creative art forms for people with autism through a comparative analysis. It explores the different approaches, methods and results of these interventions. By comparing and evaluating these programs, the study aims to identify best practices, challenges and areas for development in managing autism through the creative arts in Ghana. Although many schools and rehabilitation centres employ various forms in therapeutic approaches for autism. There is no comparative analysis of which type of autism and which creative art forms is suitable. The results of this study will contribute to the development of evidence-based practices for the management of autism in Ghana. It provides valuable information about the effectiveness of creative arts interventions and helps inform policy makers, educators, therapists and other stakeholders involved in autism support. Ultimately, the goal is to improve the well-being and quality of life of people with autism in Ghana and their families by promoting inclusive and accessible interventions that harness the power of creative art forms.

Keywords: autism, therapeutic, creative art, art form

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Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifyig the red blood cells as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively

Keywords: red blood cells, classification, radial basis function neural networks, suport vector machine, k-nearest neighbors algorithm

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Authors: Jaana Sepp, Ulvi Kõrgemaa, Kristi Puusepp, Õie Tähtla

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COVID-19 was officially recognized as a pandemic in late 2019 by the WHO, and it has led to changes in the education sector. Educational institutions were closed, and most schools adopted distance learning. Estonia is known as a digitally well-developed country. Based on that, in the pandemic time, nursing education continued, and new technological solutions were implemented. To provide nursing education, special focus was paid on quality and flexibility. The aim of this paper is to present administrative, digital, and technological solutions which support Estonian nursing educators to continue the study process in the pandemic time and to develop a sustainable solution for nursing education for the future. This paper includes the authors’ analysis of the documents and decisions implemented in the institutions through the pandemic time. It is a case study of Estonian nursing educators. Results of the analysis show that the implementation of distance learning principles challenges the development of innovative strategies and technics for the assessment of student performance and educational outcomes and implement new strategies to encourage student engagement in the virtual classroom. Additionally, hospital internships were canceled, and the simulation approach was deeply implemented as a new opportunity to develop and assess students’ practical skills. There are many other technical and administrative changes that have also been carried out, such as students’ support and assessment systems, the designing and conducting of hybrid and blended studies, etc. All services were redesigned and made more available, individual, and flexible. Hence, the feedback system was changed, the information was collected in parallel with educational activities. Experiences of nursing education during the pandemic time are widely presented in scientific literature. However, to conclude our study, authors have found evidence that solutions implemented in Estonian nursing education allowed the students to graduate within the nominal study period without any decline in education quality. Operative information system and flexibility provided the minimum distance between the students, support, and academic staff, and likewise, the changes were implemented quickly and efficiently. Institution memberships were updated with the appropriate information, and it positively affected their satisfaction, motivation, and commitment. We recommend that the feedback process and the system should be permanently changed in the future to place all members in the same information area, redefine the hospital internship process, implement hybrid learning, as well as to improve the communication system between stakeholders inside and outside the organization. The main limitation of this study relates to the size of Estonia. Nursing education is provided by two institutions only, and similarly, the number of students is low. The result could be generated to the institutions with a similar size and administrative system. In the future, the relationship between nurses’ performance and organizational outcomes should be deeply investigated and influences of the pandemic time education analyzed at workplaces.

Keywords: hybrid learning, nursing education, nursing, COVID-19

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Authors: Akrem Sellami, Imed Riadh Farah

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Hyperspectral imagery (HSI) typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image. Hence, a pixel in HSI is a high-dimensional vector of intensities with a large spectral range and a high spectral resolution. Therefore, the semantic interpretation is a challenging task of HSI analysis. We focused in this paper on object classification as HSI semantic interpretation. However, HSI classification still faces some issues, among which are the following: The spatial variability of spectral signatures, the high number of spectral bands, and the high cost of true sample labeling. Therefore, the high number of spectral bands and the low number of training samples pose the problem of the curse of dimensionality. In order to resolve this problem, we propose to introduce the process of dimensionality reduction trying to improve the classification of HSI. The presented approach is a semi-supervised band selection method based on spatial hypergraph embedding model to represent higher order relationships with different weights of the spatial neighbors corresponding to the centroid of pixel. This semi-supervised band selection has been developed to select useful bands for object classification. The presented approach is evaluated on AVIRIS and ROSIS HSIs and compared to other dimensionality reduction methods. The experimental results demonstrate the efficacy of our approach compared to many existing dimensionality reduction methods for HSI classification.

Keywords: dimensionality reduction, hyperspectral image, semantic interpretation, spatial hypergraph

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Authors: Syed Iftikhar Hussain Shah, Vasilis Peristeras, Ioannis Magnisalis

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Organizations, including governments, generate (big) data that are high in volume, velocity, veracity, and come from a variety of sources. Public Administrations are using (big) data, implementing base registries, and enforcing data sharing within the entire government to deliver (big) data related integrated services, provision of insights to users, and for good governance. Government (Big) data ecosystem actors represent distinct entities that provide data, consume data, manipulate data to offer paid services, and extend data services like data storage, hosting services to other actors. In this research work, we perform a systematic literature review. The key objectives of this paper are to propose a robust definition of government (big) data ecosystem and a classification of government (big) data ecosystem actors and their roles. We showcase a graphical view of actors, roles, and their relationship in the government (big) data ecosystem. We also discuss our research findings. We did not find too much published research articles about the government (big) data ecosystem, including its definition and classification of actors and their roles. Therefore, we lent ideas for the government (big) data ecosystem from numerous areas that include scientific research data, humanitarian data, open government data, industry data, in the literature.

Keywords: big data, big data ecosystem, classification of big data actors, big data actors roles, definition of government (big) data ecosystem, data-driven government, eGovernment, gaps in data ecosystems, government (big) data, public administration, systematic literature review

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Authors: Lori Lioce, Gary Maddux, Norven Goddard, Ishella Fogle, Bernard Schroer

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This paper presents the integration of technologies into nursing education. The collaborative effort includes the College of Nursing (CoN) at the University of Alabama in Huntsville (UAH) and the UAH Systems Management and Production Center (SMAP). The faculty at the CoN conducts needs assessments to identify education and training requirements. A team of CoN faculty and SMAP engineers then prioritize these requirements and establish improvement/development teams. The development teams consist of nurses to evaluate the models and to provide feedback and of undergraduate engineering students and their senior staff mentors from SMAP. The SMAP engineering staff develops and creates the physical models using 3D printing, silicone molds and specialized molding mixtures and techniques. The collaboration has focused on developing teaching and training, or clinical, simulators. In addition, the onset of the Covid-19 pandemic has intensified this relationship, as 3D modeling shifted to supplied personal protection equipment (PPE) to local health care providers. A secondary collaboration has been introducing students to clinical benchmarking through the UAH Center for Management and Economic Research. As a result of these successful collaborations the Model Exchange & Development of Nursing & Engineering Technology (MEDNET) has been established. MEDNET seeks to extend and expand the linkage between engineering and nursing to K-12 schools, technical schools and medical facilities in the region to the resources available from the CoN and SMAP. As an example, stereolithography (STL) files of the 3D printed models, along with the specifications to fabricate models, are available on the MEDNET website. Ten 3D printed models have been developed and are currently in use by the CoN. The following additional training simulators are currently under development:1) suture pads, 2) gelatin wound models and 3) printed wound tattoos. Specification sheets have been written for these simulations that describe the use, fabrication procedures and parts list. These specifications are available for viewing and download on MEDNET. Included in this paper are 1) descriptions of CoN, SMAP and MEDNET, 2) collaborative process used in product improvement/development, 3) 3D printed models of training and teaching simulators, 4) training simulators under development with specification sheets, 5) family care practice benchmarking, 6) integrating the simulators into the nursing curriculum, 7) utilizing MEDNET as a pandemic response, and 8) conclusions and lessons learned.

Keywords: 3D printing, nursing education, simulation, trainers

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Authors: Andrew Kanzunguze, Lusayo Mwabumba, Jason K. Gilbertson, Dominic B. Gondwe, George Z. Nxumayo

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Knowledge about the spatio-temporal distribution of invasive plants in protected areas provides a base from which hypotheses explaining proliferation of plant invasions can be made alongside development of relevant invasive plant monitoring programs. The aim of this study was to investigate the spatio-temporal distribution of bracken fern on the grassland plateau of Nyika National Park over the past 30 years (1986-2016) as well as to determine the current extent of the invasion. Remote sensing, machine learning, and statistical modelling techniques (object-based image analysis, image classification and linear regression analysis) in geographical information systems were used to determine both the spatial and temporal distribution of bracken fern in the study area. Results have revealed that bracken fern has been increasing coverage on the Nyika plateau at an estimated annual rate of 87.3 hectares since 1986. This translates to an estimated net increase of 2,573.1 hectares, which was recorded from 1,788.1 hectares (1986) to 4,361.9 hectares (2016). As of 2017 bracken fern covered 20,940.7 hectares, approximately 14.3% of the entire grassland plateau. Additionally, it was observed that the fern was distributed most densely around Chelinda camp (on the central plateau) as well as in forest verges and roadsides across the plateau. Based on these results it is recommended that Ecological Niche Modelling approaches be employed to (i) isolate the most important factors influencing bracken fern proliferation as well as (ii) identify and prioritize areas requiring immediate control interventions so as to minimize bracken fern proliferation in Nyika National Park.

Keywords: bracken fern, image classification, Landsat-8, Nyika National Park, spatio-temporal distribution

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Authors: Hsin-Yi Lo, Chia-Yu Hsu

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This article is the nursing experience of a multiple trauma case using Swanson's theory of caring, the nursing period is from May 31 to June 4, 2021, collect data through observation, written talks, interviews, listening, direct care and physical assessment, established cases with health problems such as acute pain, impaired tissue integrity, and anxiety. Nursing process including, evaluate the pain index with the pain assessment scale, assist in acupoint massage, use a corset to fix the wound, and give the patient listening to favorite radio programs to divert attention and relieve pain problems; promote wound healing and avoid infection by assessing wound condition and exudation, changing dressings with aseptic technique, and providing appropriate dressings; encourage patients to express their feelings, provide companionship, and assist in self-care and participation in treatment plans, to enable the case to overcome the anxiety caused by being admitted to the intensive care unit for the first time and not knowing about the disease, and assist the case to overcome the injury caused by the accident and return to normal life. There is no video equipment in the intensive care unit during the nursing period. In response to the problem that family visits cannot be opened during the epidemic, it is a limitation this time. It is recommended that the hospital take this into consideration in the future. In the post-epidemic era, it can reduce the risk of various infections for patients and family members. Traveling between home and hospital, improving the quality of high-quality and technological care.

Keywords: swanson's theory of caring, multiple trauma, anxiety, nursing experience

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Authors: Modi Alsubaie, Willem Kuyken, Rebecca Abbott, Barnaby Dunn, Chris Dickens, Tina Keil, William Henley

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Background: Recently, there has been an increased interest in studying the effects of mindfulness-based interventions for people with psychological and physical problems. However, the mechanisms of action in these interventions that lead to beneficial physical and psychological outcomes have yet to be clearly identified. Purpose: The aim of this paper is to review, systematically, the evidence to date on the mechanisms of action in mindfulness interventions in populations with physical and/or psychological conditions. Method: Searches of seven databases (PsycINFO, Medline (Ovid), Cochrane Central Register of Controlled Trials, EMBASE, CINAHL, AMED, ClinicalTrials.gov) were undertaken in June 2014 and July 2015. We evaluated to what extent the studies we identified met the criteria suggested by Kazdin for establishing mechanisms of action within a psychological treatment (2007, 2009). Results: We identified four trials examining mechanisms of mindfulness interventions in those with comorbid psychological and physical health problems and 14 in those with psychological conditions. These studies examined a diverse range of potential mechanisms, including mindfulness and rumination. Of these candidate mechanisms, the most consistent finding was that greater self-reported change in mindfulness mediated superior clinical outcomes. However, very few studies fully met the Kazdin criteria for examining treatment mechanisms. Conclusion: There was evidence that global changes in mindfulness are linked to better outcomes. This evidence pertained more to interventions targeting psychological rather than physical health conditions. While there is promising evidence that MBCT/MBSR intervention effects are mediated by hypothesised mechanisms, there is a lack of methodological rigour in the field of testing mechanisms of action for both MBCT and MBSR, which precludes definitive conclusions.

Keywords: MBCT, MBSR, mechanisms, physical conditions, psychological conditions, systematic review

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Authors: Kamel Belammi, Houria Fatrim

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imbalanced data set, a problem often found in real world application, can cause seriously negative effect on classification performance of machine learning algorithms. There have been many attempts at dealing with classification of imbalanced data sets. In medical diagnosis classification, we often face the imbalanced number of data samples between the classes in which there are not enough samples in rare classes. In this paper, we proposed a learning method based on a cost sensitive extension of Least Mean Square (LMS) algorithm that penalizes errors of different samples with different weight and some rules of thumb to determine those weights. After the balancing phase, we applythe different classifiers (support vector machine (SVM), k- nearest neighbor (KNN) and multilayer neuronal networks (MNN)) for balanced data set. We have also compared the obtained results before and after balancing method.

Keywords: multilayer neural networks, k- nearest neighbor, support vector machine, imbalanced medical data, least mean square algorithm, diabetes

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Authors: Ahmed Kadry, Gwyn Dodd

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There has been a growing call globally for police forces to embed evidence-based policing research methods into police interventions in order to better understand and evaluate their impact. This research study highlights the success and challenges that police forces may encounter when trying to embed evidence-based research methods within their organisation. 10 in-depth qualitative interviews were conducted with police officers and staff at Greater Manchester Police (GMP) who were tasked with integrating evidence-based research methods into their operational interventions. The findings of the study indicate that with adequate resources and individual expertise, evidence-based research methods can be applied to operational work, including the testing of initiatives with strict controls in order to fully evaluate the impact of an intervention. However, the findings also indicate that this may only be possible where an operational intervention is heavily resourced with police officers and staff who have a strong understanding of evidence-based policing research methods, attained for example through their own graduate studies. In addition, the findings reveal that ample planning time was needed to trial operational interventions that would require strict parameters for what would be tested and how it would be evaluated. In contrast, interviewees underscored that operational interventions with the need for a speedy implementation were less likely to have evidence-based research methods applied. The study contributes to the wider literature on evidence-based policing by providing considerations for police forces globally wishing to apply evidence-based research methods to more of their operational work in order to understand their impact. The study also provides considerations for academics who work closely with police forces in assisting them to embed evidence-based policing. This includes how academics can provide their expertise to police decision makers wanting to underpin their work through evidence-based research methods, such as providing guidance on how to evaluate the impact of their work with varying research methods that they may otherwise be unaware of.  

Keywords: evidence based policing, evidence-based practice, operational policing, organisational change

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Authors: Abdesselem Dakhli, Wajdi Bellil, Chokri Ben Amar

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DNA Barcode, a short mitochondrial DNA fragment, made up of three subunits; a phosphate group, sugar and nucleic bases (A, T, C, and G). They provide good sources of information needed to classify living species. Such intuition has been confirmed by many experimental results. Species classification with DNA Barcode sequences has been studied by several researchers. The classification problem assigns unknown species to known ones by analyzing their Barcode. This task has to be supported with reliable methods and algorithms. To analyze species regions or entire genomes, it becomes necessary to use similarity sequence methods. A large set of sequences can be simultaneously compared using Multiple Sequence Alignment which is known to be NP-complete. To make this type of analysis feasible, heuristics, like progressive alignment, have been developed. Another tool for similarity search against a database of sequences is BLAST, which outputs shorter regions of high similarity between a query sequence and matched sequences in the database. However, all these methods are still computationally very expensive and require significant computational infrastructure. Our goal is to build predictive models that are highly accurate and interpretable. This method permits to avoid the complex problem of form and structure in different classes of organisms. On empirical data and their classification performances are compared with other methods. Our system consists of three phases. The first is called transformation, which is composed of three steps; Electron-Ion Interaction Pseudopotential (EIIP) for the codification of DNA Barcodes, Fourier Transform and Power Spectrum Signal Processing. The second is called approximation, which is empowered by the use of Multi Llibrary Wavelet Neural Networks (MLWNN).The third is called the classification of DNA Barcodes, which is realized by applying the algorithm of hierarchical classification.

Keywords: DNA barcode, electron-ion interaction pseudopotential, Multi Library Wavelet Neural Networks (MLWNN)

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Authors: A. Elsehemy, M. Abdeen , T. Nazmy

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Since the appearance of the Semantic web, many semantic search techniques and models were proposed to exploit the information in ontology to enhance the traditional keyword-based search. Many advances were made in languages such as English, German, French and Spanish. However, other languages such as Arabic are not fully supported yet. In this paper we present a framework for ontology based information retrieval for Arabic language. Our system consists of four main modules, namely query parser, indexer, search and a ranking module. Our approach includes building a semantic index by linking ontology concepts to documents, including an annotation weight for each link, to be used in ranking the results. We also augmented the framework with an automatic document categorizer, which enhances the overall document ranking. We have built three Arabic domain ontologies: Sports, Economic and Politics as example for the Arabic language. We built a knowledge base that consists of 79 classes and more than 1456 instances. The system is evaluated using the precision and recall metrics. We have done many retrieval operations on a sample of 40,316 documents with a size 320 MB of pure text. The results show that the semantic search enhanced with text classification gives better performance results than the system without classification.

Keywords: Arabic text classification, ontology based retrieval, Arabic semantic web, information retrieval, Arabic ontology

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Authors: Joshua Midha

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The basis for information campaigns and behavioral interventions has long reigned as a tactic. From the Soviet-era propaganda machines to the opinion hijacks in Iran, these measures are now commonplace and are used for dissemination and disassembly. However, the use of these tools for strategic diffusion, specifically in a counter-terrorism setting, has only been explored on the surface. This paper aims to introduce a larger conceptual portion of guided information campaigns into preexisting terror cells and situations. It provides an alternative, low-risk intervention platform for future military strategy. This paper highlights a theoretical framework to lay out the foundationary details and explanations for behavioral interventions and moves into using a case study to highlight the possibility of implementation. It details strategies, resources, circumstances, and risk factors for intervention. It also sets an expanding foundation for offensive PsyOps and argues for tactical diffusion of information to battle extremist sentiment. The two larger frameworks touch on the internal spread of information within terror cells and external political sway, thus charting a larger holistic purpose of strategic operations.

Keywords: terrorism, behavioral intervention, propaganda, SNA, extremism

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Authors: Saurav Kumar Suman, P. Karthigayani

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In this paper the height of the tree is estimated and forest types is classified from the multi temporal RISAT-1 Horizontal-Horizontal (HH) and Horizontal-Vertical (HV) Polarised Satellite Aperture Radar (SAR) data. The novelty of the proposed project is combined use of the Back-scattering Coefficients (Sigma Naught) and the Coherence. It uses Water Cloud Model (WCM). The approaches use two main steps. (a) Extraction of the different forest parameter data from the Product.xml, BAND-META file and from Grid-xxx.txt file come with the HH & HV polarized data from the ISRO (Indian Space Research Centre). These file contains the required parameter during height estimation. (b) Calculation of the Vegetation and Ground Backscattering, Coherence and other Forest Parameters. (c) Classification of Forest Types using the ENVI 5.0 Tool and ROI (Region of Interest) calculation.

Keywords: RISAT-1, classification, forest, SAR data

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Authors: Merle Talvik, Taimi Tulva, Kristi Puusepp, Ülle Ernits

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Background data. In 1940–1941 and 1945–1991 Estonia was occupied by Soviet Union. Paternalism was a common principle in Soviet social policy, including health care. The Soviet government, not the individuals themselves, decided on achieving a person’s quality of life. With the help of Soviet ideology, the work culture of nurses was constructed and the education system was also reshaped according to the ideology. The “new period of awakening” was initiated under Gorbachev’s perestroika and glasnost (1985–1991), leading to democratization. Aim. The qualitative study aimed to analyze nursing education in Soviet Estonia in the conditions of paternalistic orientation and ideological pressure. Method. The research was conducted in 2021 and 2023. Senior nurses (aged 69–87) who had worked for at least 20 years during the Soviet era were surveyed. Thematic interviews were conducted in written form and orally (13 interviewees), followed by a focus group interview (8 interviewees). A thematic content analysis was performed. Results. Nursing is part of society’s culture and in this sense, in - terviews with nurses provide us with critical information about the functioning of society and cultural identity at a given time. During the Soviet era the training of nurses occured within vocational training institutions. The curricula underwent a shift towards a Soviet-oriented approach. A significant portion of lessons were dedicated to imparting knowledge on the principles and tenets of Communist-Marxist ideology. Therefore, practical subjects and nursing theory were frequently allocated limited space. A paternalistic orientation prevailed in health care: just as the state regulated how to cure, spread hygiene, and healthy lifestyles propaganda, training was also determined by the management of the institution, thereby limiting the person´s autonomy to decide what kind of training was needed. The research is of significant value in the context of the history of nursing, as it helps to understand the difficulties and complexity of the development of nursing on the timeline. The Soviet era still affects Estonian society today and will continue to do so in the future. The same type of developments occurred in other post-Soviet countries.

Keywords: Estonian SSR, nursing education, paternalism, senior nurse, Soviet ideology

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Authors: T. O. Adeoye

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In Nigeria prior to the arrival of the colonial masters, there used to be good family relationship, things were done through cooperation and community effort, even training and upbringing of a child was the responsibility of every member of the community. The advent of western education and civilization brought about movement of youths from rural to urban areas. Hence the tie that binds the village life is broken, community as a whole is no more responsible for the upbringing or home training of her children any more. The study investigates adoption of home nursing as a remedy for child labour in Nigeria, considered challenges like poverty, broken homes and illiteracy among others. For the purpose of verification, a survey in form of an opinion poll was conducted by means of interview using questionnaires for about 3000 respondents. However, out of the respondents that were interviewed as regard parental poverty and its resultant effect on a child only 14.2% of the respondent strongly disagreed that children work on the street because their parents are poor. The study shows that possible solutions of effective ways of controlling child labour is home nursing and also promoting and advocating for the policies against child labour in Nigeria.

Keywords: child abuse, child labour, psychological problem, Street children

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Authors: Sondos M. Hammad, Sherin M. ElGokhy, Mahmoud M. Fahmy, Elsayed A. Sallam

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In this paper, an ensemble-based Support Vector Machine (SVM) classification approach is proposed. It is used for miRNA prediction. Three problems, commonly associated with previous approaches, are alleviated. These problems arise due to impose assumptions on the secondary structural of premiRNA, imbalance between the numbers of the laboratory checked miRNAs and the pseudo-hairpins, and finally using a training data set that does not consider all the varieties of samples in different species. We aggregate the predicted outputs of three well-known SVM classifiers; namely, Triplet-SVM, Virgo and Mirident, weighted by their variant features without any structural assumptions. An additional SVM layer is used in aggregating the final output. The proposed approach is trained and then tested with balanced data sets. The results of the proposed approach outperform the three base classifiers. Improved values for the metrics of 88.88% f-score, 92.73% accuracy, 90.64% precision, 96.64% specificity, 87.2% sensitivity, and the area under the ROC curve is 0.91 are achieved.

Keywords: MiRNAs, SVM classification, ensemble algorithm, assumption problem, imbalance data

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Authors: Betül sönmez, Fatma Azizoğlu, S. Bilge Hapçıoğlu, Aytolan Yıldırım

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Background: Social innovativeness contains the procurement of a sustainable benefit for a number of problems from working conditions to education, social development, health, and from environmental control to climate change, as well as the development of new social productions and services. Objectives: This study was conducted to determine the correlation between the social innovation tendency of nursing and medical faculty students and value types. Methods and participants: The population of this correlational study consisted of third-year students studying at a medical faculty and a nursing faculty in a public university in Istanbul. Ethics committee approval and permission from the school administrations were obtained in order to conduct the study and voluntary participation of the students in the study was ensured. 524 questionnaires were obtained with a total return rate of 57.1% (65.0% in nurse student and 52.1% in physic students). The data of the study were collected by using the Portrait Values Questionnaire and a questionnaire containing the Social Innovativeness Scale. Results: The effect of the subscale scores of Portrait Values Questionnaire on the total score of Social Innovativeness Scale was 26.6%. In the model where a significance was determined (F=37.566; p<0.01), the highest effect was observed in the subscale of universalism. The effect of subscale scores obtained from the Portrait Values Questionnaire, as well as age, gender and number of siblings was 25% on the Social Innovativeness in nursing students and 30.8% in medical faculty students. In both models where a significance was determined (p<0.01), the nursing students had the values of power, universalism and kindness, whereas the medical faculty students had the values of self-direction, stimulation, hedonism and universalism showed the highest effect in both models. Conclusions: Universalism is the value with the highest effect upon the social innovativeness in both groups, which is an expected result by the nature of professions. The effect of the values of independent thinking and self-direction, as well as openness to change involving quest for innovation (stimulation), which are observed in medical faculty students, also supports the literature of innovative behavior. These results are thought to guide educators and administrators in terms of developing socially innovative behaviors.

Keywords: social innovativeness, portrait values questionnaire, nursing students, medical faculty students

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Authors: Cuneyt Yucelbas, Seral Ozsen, Sule Yucelbas, Gulay Tezel

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Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems.

Keywords: artificial immune system, breast cancer diagnosis, Euclidean function, Gaussian function

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Authors: Jeremy Bekker, Guy Salazar

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Introduction: Nature-exposure interventions on university campuses may serve as an effective addition to overburdened counseling and student support centers. Nature-exposure interventions can work as a preventative well-being enhancement measure on campuses, which can be used adjacently with existing health resources. Specifically, this paper analyzes how spending time in nature impacts psychological well-being, cognitive functioning, and physical health. The poster covers the core findings and recommendations of this paper, which has been previously published in the BYU undergraduate psychology journal Intuition. Research Goals and Method: The goal of this paper was to outline the potential benefits of nature exposure for students’ physical health, mental well-being, and academic success. Another objective of this paper was to outline potential research-based interventions that use campus green spaces to improve student outcomes. Given that the core objective of this paper was to identify and establish research-based nature exposure interventions that could be used on college campuses, a broad literature review focused on these areas. Specifically, the databases Scopus and PsycINFO were used to screen for research focused on psychological well-being, physical health, cognitive functioning, and nature exposure interventions. Outcomes: Nature exposure has been shown to help increase positive affect, life satisfaction, happiness, coping ability and subjective well-being. Further, nature exposure has been shown to decrease negative affect, lower mental distress, reduce cognitive load, and decrease negative psychological symptoms. Finally, nature exposure has been shown to lead to better physical health. Findings and Recommendations: Potential interventions include adding green space to university buildings and grounds, dedicating already natural environments as nature restoration areas, and providing means for outdoor excursions. Potential limitations and suggested areas for future research are also addressed. Many campuses already contain green spaces, defined as any part of an environment that is predominately made of natural elements, and these green spaces comprise an untapped resource that is relatively cheap and simple.

Keywords: nature exposure, preventative care, undergraduate mental health, well-being intervention

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Authors: Maria Luiza Fulga, Georgeta Truca, Mihaela Alexandru, Andriescu Mariana, Crin Marcean

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The significance of nursing as a subject in the post-secondary healthcare curriculum is a major. We aimed to enable our students to assess the patient's risk, to establish prevention measures and to adapt to a specific learning context, in order to acquire the skills and abilities necessary for the nursing profession. In order to achieve these objectives, during the three years of study, teachers put an emphasis on acquiring communication skills, because in our country after the first cycle of hospital accreditation concluded in 2016, the National Authority for Quality of Health Management has introduced the criteria for the implementation and application of the nursing process according to the accreditation standards. According to these requirements, the nurse has to carry out the nursing assessment, based on communication as a distinct component, so that they can identify nursing diagnoses and implement the nursing plan. In this respect, we, the teachers, have refocused, by approaching various teaching strategies and preparing students for the real context of learning and applying what they learn. In the educational process, the tutors in the hospitals have an important role to play in acquiring professional skills. Students perform their activity in the hospital in accordance with the curriculum, in order to verify the practical applicability of the theoretical knowledge acquired in the school classes and also have the opportunity to acquire their skills in a real learning context. In clinical education, the student nurse learns in the middle of a guidance team which includes a practice tutor, who is a nurse that takes responsibility for the practical/clinical learning of the students in their field of activity. In achieving this objective, the tutor's abilities involve pedagogical knowledge, knowledge for the good of the individual and nursing theory, in order to be able to guide clinical practice in accordance with current requirements. The aim of this study is to find out the students’ confidence level in practice tutors in hospitals, the students’ degree of satisfaction in the pedagogical skills of the tutors and the practical applicability of the theoretical knowledge. In this study, we used as a method of investigation a student satisfaction questionnaire regarding the clinical practice in the hospital and the sample of the survey consisted of 100 students aged between 20 and 50 years, from the first, second and third year groups, with the General Nurse specialty (nurses responsible for general care), from 'Fundeni' Healthcare Post-Secondary School, Bucharest, Romania. Following the analysis of the data provided, we arrived the conclusion that the hospital tutor needs to improve his/her pedagogical skills, the knowledge of nursing diagnostics, and the implementation of the nursing plan, so that the applicability of the theoretical notions would be increased. Future plans include the pedagogical training of the medical staff, as well as updating the knowledge needed to implement the nursing process in order to meet current requirements.

Keywords: clinical training, nursing process, pedagogical skills, tutor

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Authors: Richard Vuijk, Arnoud Arntz

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Rationale: To our knowledge treatment of personality disorder comorbidity in adults with autism spectrum disorder (ASD) is understudied and is still in its infancy: We do not know if treatment of personality disorders may be applicable to adults with ASD. In particular, it is unknown whether patients with ASD benefit from experiential techniques that are part of schema therapy developed for the treatment of personality disorders. Objective: The aim of the study is to investigate the efficacy of a schema mode focused treatment with adult clients with ASD and comorbid personality pathology (i.e. at least one personality disorder). Specifically, we investigate if they can benefit from both cognitive-behavioral, and experiential interventions. Study design: A multiple baseline case series study. Study population: Adult individuals (age > 21 years) with ASD and at least one personality disorder. Participants will be recruited from Sarr expertise center for autism in Rotterdam. The study requires 12 participants. Intervention: The treatment protocol consists of 35 weekly offered sessions, followed by 10 monthly booster sessions. A multiple baseline design will be used with baseline varying from 5 to 10 weeks, with weekly supportive sessions. After baseline, a 5-week exploration phase follows with weekly sessions during which current and past functioning, psychological symptoms, schema modes are explored, and information about the treatment will be given. Then 15 weekly sessions with cognitive-behavioral interventions and 15 weekly sessions with experiential interventions will be given. Finally, there will be a 10-month follow-up phase with monthly booster sessions. Participants are randomly assigned to baseline length, and respond weekly during treatment and monthly at follow-up on Belief Strength of negative core beliefs (by VAS), and fill out SMI, SCL-90 and SRS-A 7 times during screening procedure (i.e. before baseline), after baseline, after exploration, after cognitive and behavioral interventions, after experiential interventions, and after 5- and 10- month follow-up. The SCID-II will be administered during screening procedure (i.e. before baseline), at 5- and at 10-month follow-up. Main study parameters: The primary study parameter is negative core beliefs. Secondary study parameters include schema modes, personality disorder manifestations, psychological symptoms, and social interaction and communication. Discussion: To the best of author’s knowledge so far no study has been published on the application of schema mode focused interventions in adult patients with ASD and comorbid PD(s). This study offers the first systematic test of application of schema therapy for adults with ASD. The results of this study will provide initial evidence for the effectiveness of schema therapy in treating adults with both ASD and PD(s). The study intends to provide valuable information for future development and implementation of therapeutic interventions for adults with both ASD and PD(s).

Keywords: adults, autism spectrum disorder, personality disorder, schema therapy

Procedia PDF Downloads 215