Search results for: nursing interventions classification

Authors: Mustafa Alhamdi

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Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

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Authors: Abeer Alhazmi

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Introduction: Clinical education is considered a significant part of the learning process for nurses and nursing students. However, recruiting high- caliber individuals to train them to be tomorrow’s educators/teachers has been a recurrent challenge. One of the troubling challenges in this field is the absent of proper training programmes to train educators to be future education professionals and leaders. Aim: To explore the impact of a stage 1 and stage 2 clinical instructor courses on developing leadership skills for nurses as educators.Theoretical Framework: Informed by a symbolic interactionist framework, this research explored the Impact of stage 1 and stage 2 clinical instructor courses on nurses' knowledge, attitudes, and leadership skills. Method: Using Glaserian grounded theory method the data were derived from 3 focus groups and 15 in-depth interviews with nurse educators/clinical instructors and nurses who attended stage 1 and stage 2 clinical instructor courses at King Abdu-Aziz University Hospital (KAUH). Findings: The findings of the research are represented in the core category exploring new identity as educator and its two constituent categories Accepting change, and constructing educator identity. The core and sub- categories were generated through a theoretical exploration of the development of educator’s identity throughout stage 1 and stage 2 clinical instructor courses. Conclusion: The social identity of the nurse educators was developed and changed during and after attending stage 1 and stage 2 clinical instructor courses. In light of an increased understanding of the development process of educators identity and role, the research presents implications and recommendations that may contribute to the development of nursing educators in general and in Saudi Arabia in specific.

Keywords: clinical instructor course, educators, identity work, clinical nursing

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Authors: Yoshimoto Kurihara, Tad Gonsalves

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Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.

Keywords: 6D posture estimation, image recognition, deep learning, AlexNet

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Authors: Ronaldo Seroa Da Motta, Gabriela Santiago

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The main contribution of this study is the identification of the factors influencing the environmental licensing process of infrastructure projects in Brazil. These factors will be those that reflect the technical characteristics of the project, the corporate governance of the entrepreneur, and the institutional and regulatory governance of the environmental agency, including the number of interventions by non-licensing agencies. The model conditions these variables to the licensing processing time of 34 infrastructure projects. Our results indicated that the conditions would be more sensitive to the type of enterprise, complexity as in gas pipelines and hydroelectric plants in the most vulnerable biome with a greater value of the enterprise or the entrepreneur's assets, together with the number of employees of the licensing agency. The number of external interventions by other non-licensing institutions does not affect the licensing time. Such results challenge the current criticism that environmental licensing has been often pointed out as a barrier to speed up investments in infrastructure projects in Brazil due to the participation of civil society and other non-licensing institutions.

Keywords: environmental licensing, condionants, Brazil, timing process

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Authors: Justina Ijeoma Ezebuwa, Catherine C. Ikewuchi, Eka B. Essien, Francis C. Anacletus

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Iron deficiency anemia (IDA) is a significant problem in Nigeria, especially in children under five. Intervention options for treating anemia in under-five children have also been the subject of research. This study aims to synthesize the evidence on dietary interventions for managing iron deficiency anemia in under-five children in Nigeria. This study uses a systematic review method to collect relevant studies to answer the research questions and a narrative synthesis to analyze and synthesize the findings of this study. Cochrane, PubMed, and ScienceDirect databases were thoroughly searched, and five (5) articles were selected for this study. The results show that dietary interventions, such as daily multi-nutrient fortified dairy-based drinks, biofortified (yellow/ white) cassava rich in pro-vitamin A, iron supplementation in foods, and ready-to-use therapeutic food (RUTF) may be relevant to the management of iron deficiency anemia in under-five children in Nigeria. The study also shows that daily multi-nutrient fortified dairy-based drinks may be more effective, feasible, and culturally acceptable for managing anemia in under-5 children in Nigeria. In addition, daily multi-nutrient fortified dairy-based drinks and ready-to-use therapeutic food (RUTF) were reported to have the potential to improve the growth, cognitive development, and overall health outcomes of under-five children with iron deficiency anemia in Nigeria. Recommendations for future studies in this research area and for public health practitioners and policymakers were provided.

Keywords: dietary intervention, iron deficiency anemia, under-five children, Nigeria

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Authors: Xiaoqi Jia, Qing Zhu, Hao Zhang, Su Yang

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A gender recognition system is able to tell the gender of the given person through a few of frontal facial images. An effective gender recognition approach enables to improve the performance of many other applications, including security monitoring, human-computer interaction, image or video retrieval and so on. In this paper, we present an effective method for gender classification task in frontal facial images based on deep belief networks (DBNs), which can pre-train model and improve accuracy a little bit. Our experiments have shown that the pre-training method with DBNs for gender classification task is feasible and achieves a little improvement of accuracy on FERET and CAS-PEAL-R1 facial datasets.

Keywords: gender recognition, beep belief net-works, semi-supervised learning, greedy-layer wise RBMs

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Authors: Mokgophana Ramasobana

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Background: At the core of the demand to utilize evidence-based approaches in the policy-making cycle, prioritization of limited financial resources and results driven initiatives is the urgency to develop a cohort of competent Monitoring and Evaluation (M&E) practitioners and public servants. The ongoing strides in the evaluation capacity building (ECB) initiatives are a direct response to produce the highly-sought after M&E skills. Notwithstanding the rapid growth of M&E short courses, participants perceived value and expectation of M&E short courses as a panacea for ECB have not been empirically quantified or measured. The objective of this article is to explicitly illustrate the importance of measuring ECB interventions and understanding what works in ECB and why it works. Objectives: This article illustrates the importance of establishing empirical ECB measurement tools to evaluate ECB interventions in order to ascertain its contribution to the broader evaluation practice. Method: The study was primarily a desktop review of existing literature, juxtaposed by a survey of the participants across the African continent based on the 43 M&E short courses hosted by the Centre for Learning on Evaluation and Results Anglophone Africa (CLEAR-AA) in collaboration with the Department of Planning Monitoring and Evaluation (DPME) Results: The article established that participants perceive short course training as a panacea to improve their M&E practical skill critical to executing their organizational duties. In tandem, participants are likely to demand customized training as opposed to general topics in Evaluation. However, the organizational environments constrain the application of the newly acquired skills. Conclusion: This article aims to contribute to the 'how to' measure ECB interventions discourse and contribute towards the improvement to evaluate ECB interventions. The study finds that participants prefer training courses with longer duration to cover more topics. At the same time, whilst organizations call for customization of programmes, the study found that individual participants demand knowledge of generic and popular evaluation topics.

Keywords: evaluation capacity building, effectiveness and training, monitoring and evaluation (M&E) short course training, perceptions and expectations

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Authors: Kimberli Breen

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Background Check-In, Check-Out is noted as the most widely implemented evidence-based intervention for youth at-promise within schools. Over twenty years of peer-reviewed research demonstrates the powerful effects of this Positive Behavioral Interventions and Supports (PBIS) practice when implemented with fidelity. However, literature to date has not explicitly connected this intervention with Positive Psychology. Aims This session will illustrate the powerful role Positive Psychology and core elements of PERMA play in the worldwide success of this intervention and how more explicitly aligning Positive Behavioral Interventions and Supports (PBIS) practices with Positive Psychology might remove common barriers to current implementation. Method Students receiving the Check-In, Check-Out intervention experience a warm, positive greeting from a caring adult (CICO Coach) before entering their first class of the day. Teachers then provide high frequency positive feedback to the students at the end of each time block, or segment, of the day. An “optimistic close” to the day is then provided by the same CICO Coach at the end of the school day via the “check-out” process, where students assess the day’s accomplishments and goal-set for the next day. Results CICO clearly aligns with the Positive Psychology core elements of PERMA (Positive Emotion, Engagement, Relationships, Meaning and Accomplishments) and could be further strengthened through explicit integration. Conclusion The already powerful impact and reach of the Check-In, Check-Out intervention can be further enhanced and expanded through greater alignment with Positive Psychology elements and practices. Initiating this important alignment with CICO also offers promise for further integration of Positive Psychology and Positive Behavioral Interventions and Supports.

Keywords: positive pscyhology, check-In check-out, schools, alignment

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Authors: Oumaima Khlifati, Khadija Baba

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Pavement distress is the main factor responsible for the deterioration of road structure durability, damage vehicles, and driver comfort. Transportation agencies spend a high proportion of their funds on pavement monitoring and maintenance. The auscultation of pavement distress was based on the manual survey, which was extremely time consuming, labor intensive, and required domain expertise. Therefore, the automatic distress detection is needed to reduce the cost of manual inspection and avoid more serious damage by implementing the appropriate remediation actions at the right time. Inspired by recent deep learning applications, this paper proposes an algorithm for automatic road distress detection and classification using on the Deep Convolutional Neural Network (DCNN). In this study, the types of pavement distress are classified as transverse or longitudinal cracking, alligator, pothole, and intact pavement. The dataset used in this work is composed of public asphalt pavement images. In order to learn the structure of the different type of distress, the DCNN models are trained and tested as a multi-label classification task. In addition, to get the highest accuracy for our model, we adjust the structural optimization hyper parameters such as the number of convolutions and max pooling, filers, size of filters, loss functions, activation functions, and optimizer and fine-tuning hyper parameters that conclude batch size and learning rate. The optimization of the model is executed by checking all feasible combinations and selecting the best performing one. The model, after being optimized, performance metrics is calculated, which describe the training and validation accuracies, precision, recall, and F1 score.

Keywords: distress pavement, hyperparameters, automatic classification, deep learning

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Authors: Qing Wu, Fei-Yan Li, Heng-Chang Zhang

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Multitask learning support vector machines (SVMs) have recently attracted increasing research attention. Given several related tasks, the single-task learning methods trains each task separately and ignore the inner cross-relationship among tasks. However, multitask learning can capture the correlation information among tasks and achieve better performance by training all tasks simultaneously. In addition, the asymmetric squared loss function can better improve the generalization ability of the models on the most asymmetric distributed data. In this paper, we first make two assumptions on the relatedness among tasks and propose two multitask learning proximal support vector machine algorithms, named MTL-a-PSVM and EMTL-a-PSVM, respectively. MTL-a-PSVM seeks a trade-off between the maximum expectile distance for each task model and the closeness of each task model to the general model. As an extension of the MTL-a-PSVM, EMTL-a-PSVM can select appropriate kernel functions for shared information and private information. Besides, two corresponding special cases named MTL-PSVM and EMTLPSVM are proposed by analyzing the asymmetric squared loss function, which can be easily implemented by solving linear systems. Experimental analysis of three classification datasets demonstrates the effectiveness and superiority of our proposed multitask learning algorithms.

Keywords: multitask learning, asymmetric squared loss, EMTL-a-PSVM, classification

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: Subhadeep Mandal, Sujoy Bhattacharya, Pabitra Mitra, Diya Guha Roy, Seema Bhattacharya

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E-commerce firms allow customers to evaluate and review the things they buy as a positive or bad experience. The e-commerce transaction processes are made up of a variety of diverse organizations and activities that operate independently but are connected together to complete the transaction (from placing an order to the goods reaching the client). After a negative shopping experience, clients frequently disregard the critical assessment of these businesses and submit their feedback on an all-over basis, which benefits certain enterprises but is tedious for others. In this article, we solely dealt with negative reviews and attempted to distinguish between negative reviews where the e-commerce firm is explicitly blamed by customers for a bad purchasing experience and other negative reviews.

Keywords: e-commerce, online shopping, customer reviews, customer behaviour, text analytics, n-grams classification

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Authors: Eugene Y. J. Aw, J. W. Koh, S. H. Chew, K. E. Chua, P. L. Goh, Grace H. B. Foo, M. L. Leong

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This paper presents the evaluation of various soil testing methods such as the four-probe soil electrical resistivity method and cone penetration test (CPT) that can complement a newly developed novel rapid soil classification scheme using computer vision, to improve the accuracy and productivity of on-site classification of excavated soil. In Singapore, excavated soils from the local construction industry are transported to Staging Grounds (SGs) to be reused as fill material for land reclamation. Excavated soils are mainly categorized into two groups (“Good Earth” and “Soft Clay”) based on particle size distribution (PSD) and water content (w) from soil investigation reports and on-site visual survey, such that proper treatment and usage can be exercised. However, this process is time-consuming and labor-intensive. Thus, a rapid classification method is needed at the SGs. Four-probe soil electrical resistivity and CPT were evaluated for their feasibility as suitable additions to the computer vision system to further develop this innovative non-destructive and instantaneous classification method. The computer vision technique comprises soil image acquisition using an industrial-grade camera; image processing and analysis via calculation of Grey Level Co-occurrence Matrix (GLCM) textural parameters; and decision-making using an Artificial Neural Network (ANN). It was found from the previous study that the ANN model coupled with ρ can classify soils into “Good Earth” and “Soft Clay” in less than a minute, with an accuracy of 85% based on selected representative soil images. To further improve the technique, the following three items were targeted to be added onto the computer vision scheme: the apparent electrical resistivity of soil (ρ) measured using a set of four probes arranged in Wenner’s array, the soil strength measured using a modified mini cone penetrometer, and w measured using a set of time-domain reflectometry (TDR) probes. Laboratory proof-of-concept was conducted through a series of seven tests with three types of soils – “Good Earth”, “Soft Clay,” and a mix of the two. Validation was performed against the PSD and w of each soil type obtained from conventional laboratory tests. The results show that ρ, w and CPT measurements can be collectively analyzed to classify soils into “Good Earth” or “Soft Clay” and are feasible as complementing methods to the computer vision system.

Keywords: computer vision technique, cone penetration test, electrical resistivity, rapid and non-destructive, soil classification

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Authors: Jamshid Qodirov, Sirojiddin Komolov, Ravilov Mirahmad, Olimjon Mirzayev

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Nowadays, natural language processing tools play a crucial role in our daily lives, including various techniques with text processing. There are very advanced models in modern languages, such as English, Russian etc. But, in some languages, such as Uzbek, the NLP models have been developed recently. Thus, there are only a few NLP models in Uzbek language. Moreover, there is no such work that could show which Uzbek NLP model behaves in different situations and when to use them. This work tries to close this gap and compares the Uzbek NLP models existing as of the time this article was written. The authors try to compare the NLP models in two different scenarios: sentiment analysis and sentence similarity, which are the implementations of the two most common problems in the industry: classification and similarity. Another outcome from this work is two datasets for classification and sentence similarity in Uzbek language that we generated ourselves and can be useful in both industry and academia as well.

Keywords: NLP, benchmak, bert, vectorization

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Authors: E. Grochowska-Niedworok, K. Brukalo, B. Całyniuk, J. Piekorz, M. Kardas

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The aim of the study was to assess diets of residents of nursing homes. Provided by social welfare home, 10 day menus were introduced into the computer program Diet 5 and analyzed in respect of protein, fats, carbohydrates, energy, vitamin D and calcium. The resulting mean values of 10-day menus were compared with the existing Nutrition Standards for Polish population. The analysis menus showed that the average amount of energy supplied from food is not sufficient. Carbohydrates in food supply are too high and represent 257% of normal. The average value of fats and proteins supplied with food is adequate 85.2 g/day and 75.2 g/day. The calcium content of the diet is 513.9 mg/day. The amount of vitamin D supplied in the age group 51-65 years is 2.3 µg/day. Dietary errors that have been shown are due to the lack of detailed nutritional guidelines for nursing homes, as well as state-owned care facilities in general.

Keywords: assessment of diet, essential nutrients, social welfare home, nutrition

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Authors: Scarlet Tabot Enanga Longsti

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Community-Based Health Insurance (CBHI) schemes have been proposed as a possible means to achieve affordable health care in low-and lower-middle-income countries. The existing evidence provides mixed results on the impact of CBHI schemes on healthcare utilisation and out -of-pocket payments (OOPP) for healthcare. Over 900 CBHI schemes have been implemented in underdeveloped countries, and these schemes have undergone different modifications over the years. Prior reviews have suggested that different designs of CBHI schemes may result in different outcomes. Objectives: This review sought to determine the interventions that affect the impact of CBHI schemes on OOPP and health service utilisation. Interventions in this study referred to any action or modification in the design of a CBHI scheme that affected the impact of the scheme on OOPP and/or healthcare utilization. Methods: Any CBHI study that was done in a lower middle-income country, that used an experimental design, that included OOPP or health care utilisation as outcome variables, and that was published in either English or French was included in this study. Studies were searched for in MEDLINE, Embase, CINAHL, EconLit, IBSS, Web of Science, Cochrane Library, and Global Index Medicus from July to August 2023. Bias was assessed using Joanna Brigs Institute tools for quality assessment for randomized control trials and quasi experimental studies. A narrative synthesis was done. Results: 12 studies were included in the review, with a total of 69 villages, 13,653 households, and 62,786 participants. Average premium collection was 4.8 USD/year. Most CBHI schemes had flat rates. The study revealed that a range of interventions impact OOPP and health care utilisation. Five categories of interventions were identified. The intervention with the highest impact on OOPP and utilisation was “Audit visits”. Next in line came external funds, training scheme workers, and engaging community leaders and village heads to advertise the scheme. Free healthcare led to a significant increase in utilisation of health services, a significant reduction in Catastrophic health expenditure, but an insignificant effect on OOPP among insured compared with uninsured. Conclusions: Community-Based Health Insurance could pave the way for Universal Health Care in low and middle-income countries. However, this can only be possible if careful thought is given to how schemes are designed. Due to the heterogeneity of studies and results on CBHI schemes, there is need for further research for more effective designs to be developed.

Keywords: community based health insurance, developing countries, health service utilisation, out of pocket payment

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Authors: Ma Cecilia Siva

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This study introduces a Bidirectional Encoder Representations from Transformers (BERT)-based machine learning model aimed at improving educational counseling by automating the process of recommending academic strands for students. The framework is designed to streamline and enhance the strand selection process by analyzing students' profiles and suggesting suitable academic paths based on their interests, strengths, and goals. Data was gathered from a sample of 200 grade 10 students, which included personal essays and survey responses relevant to strand alignment. After thorough preprocessing, the text data was tokenized, label-encoded, and input into a fine-tuned BERT model set up for multi-label classification. The model was optimized for balanced accuracy and computational efficiency, featuring a multi-category classification layer with sigmoid activation for independent strand predictions. Performance metrics showed an F1 score of 88%, indicating a well-balanced model with precision at 80% and recall at 100%, demonstrating its effectiveness in providing reliable recommendations while reducing irrelevant strand suggestions. To facilitate practical use, the final deployment phase created a recommendation framework that processes new student data through the trained model and generates personalized academic strand suggestions. This automated recommendation system presents a scalable solution for academic guidance, potentially enhancing student satisfaction and alignment with educational objectives. The study's findings indicate that expanding the data set, integrating additional features, and refining the model iteratively could improve the framework's accuracy and broaden its applicability in various educational contexts.

Keywords: tokenized, sigmoid activation, transformer, multi category classification

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Authors: Ashleigh Beales, Anne Whitworth, Jade Cartwright

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Background: While significant benefits of lexical retrieval intervention are evident within the Primary Progressive Aphasia (PPA) and Alzheimer’s disease (AD) literature, an understanding of the mechanisms that underlie change or improvement is limited. Change mechanisms have been explored in the non-progressive post-stroke literature that may offer insight into how interventions affect change with progressive language disorders. The potential influences of cognitive factors may also play a role here, interacting with the aims of intervention. Exploring how such processes have been applied is likely to grow our understanding of how interventions have, or have not, been effective, and how and why generalisation is likely, or not, to occur. Aims: This review of the literature aimed to (1) investigate the proposed mechanisms of change which underpin lexical interventions, mapping the PPA and AD lexical retrieval literature to theoretical accounts of mechanisms that underlie change within the broader intervention literature, (2) identify whether and which nonlinguistic cognitive functions have been engaged in intervention with these populations and any proposed influence, and (3) explore evidence of linguistic generalisation, with particular reference to change mechanisms employed in interventions. Main contribution: A search of Medline, PsycINFO, and CINAHL identified 36 articles that reported data for individuals with PPA or AD following lexical retrieval intervention. A review of the mechanisms of change identified 10 studies that used stimulation, 21 studies utilised relearning, three studies drew on reorganisation, and two studies used cognitive-relay. Significant treatment gains, predominantly based on linguistic performance measures, were reported for all client groups for each of the proposed mechanisms. Reorganisation and cognitive-relay change mechanisms were only targeted in PPA. Eighteen studies incorporated nonlinguistic cognitive functions in intervention; these were limited to autobiographical memory (16 studies), episodic memory (three studies), or both (one study). Linguistic generalisation outcomes were inconsistently reported in PPA and AD studies. Conclusion: This review highlights that individuals with PPA and AD may benefit from lexical retrieval intervention, irrespective of the mechanism of change. Thorough application of a theory of intervention is required to gain a greater understanding of the change mechanisms, as well as the interplay of nonlinguistic cognitive functions.

Keywords: Alzheimer's disease, lexical retrieval, mechanisms of change, primary progressive aphasia

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Authors: Natthawut Arin

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In forensic contexts, competency to stand trial assessments are the most common referrals. The defendants may attempt to endorse psychopathology symptoms and feign incompetent. Coaching, which can be teaching them test-taking strategies to avoid detection of psychopathological symptoms feigning. Recently, the Symptom Validity Testings (SVTs) were created to detect feigning. Moreover, the works of the literature showed that the effects of coaching on SVTs may be more robust to the effects of coaching. Thai Symptom Validity Test (SVT-Th) was designed as SVTs which demonstrated adequate psychometric properties and ability to classify between feigners and honest responders. Thus, the current study to examine the utility as the robustness of SVT-Th in the detection of feigned psychopathology. Participants consisted of 120 were recruited from undergraduate courses in psychology, randomly assigned to one of three groups. The SVT-Th was administered to those three scenario-experimental groups: (a) Uncoached group were asked to respond honestly (n=40), (b) Symptom-coached without warning group were asked to feign psychiatric symptoms to gain incompetency to stand trial (n=40), while (c) Test-coached with warning group were asked to feign psychiatric symptoms to avoid test detection but being incompetency to stand trial (n=40). Group differences were analyzed using one-way ANOVAs. The result revealed an uncoached group (M = 4.23, SD.= 5.20) had significantly lower SVT-Th mean scores than those both coached groups (M =185.00, SD.= 72.88 and M = 132.10, SD.= 54.06, respectively). Classification rates were calculated to determine the classification accuracy. Result indicated that SVT-Th had overall classification accuracy rates of 96.67% with acceptable of 95% sensitivity and 100% specificity rates. Overall, the results of the present study indicate that the SVT-Th yielded high adequate indices of accuracy and these findings suggest that the SVT-Th is robustness against coaching.

Keywords: incompetency to stand trial, coaching, robustness, classification accuracy

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Authors: Songul Cinaroglu

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Background: Random Forest is an efficient, multi-class machine learning method using for classification, regression and other tasks. This method is operating by constructing each tree using different bootstrap sample of the data. Determining the number of trees in random forests is an open question in the literature for studies about improving classification performance of random forests. Aim: The aim of this study is to analyze whether there is an optimal number of trees in Random Forests and how performance of Random Forests differ according to increase in number of trees using sample health data sets in R programme. Method: In this study we analyzed the performance of Random Forests as the number of trees grows and doubling the number of trees at every iteration using “random forest” package in R programme. For determining minimum and optimal number of trees we performed Mc Nemar test and Area Under ROC Curve respectively. Results: At the end of the analysis it was found that as the number of trees grows, it does not always means that the performance of the forest is better than forests which have fever trees. In other words larger number of trees only increases computational costs but not increases performance results. Conclusion: Despite general practice in using random forests is to generate large number of trees for having high performance results, this study shows that increasing number of trees doesn’t always improves performance. Future studies can compare different kinds of data sets and different performance measures to test whether Random Forest performance results change as number of trees increase or not.

Keywords: classification methods, decision trees, number of trees, random forest

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Authors: Güler Duru Aşiret

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Introduction: Poor sleep quality is a common problem among the older people living in nursing homes. Our study aimed at assessing the effect of individual reminiscence therapy on the sleep quality of the elderly living in nursing homes. Methods: The study had 22 people in the intervention group and 24 people in the control group. The intervention group had reminiscence therapy once a week for 12 weeks in the form of individual sessions of 25-30 minutes. In our study, we first determined the dates suitable for the intervention group and researcher and planned the date and time of individual reminiscence therapies, which would take 12 weeks. While preparing this schedule, we considered subjects’ time schedules for their regular visits to health facilities and the arrival of their visitors. At this stage, the researcher informed the participants that their regular attendance in sessions would affect the intervention outcome. One topic was discussed every week. Weekly topics included: introduction in the first week; childhood and family life, school days, starting work and work life (a day at home for housewives), a fun day out of home, marriage (friendship for the singles), plants and animals they loved, babies and children, food and cooking, holidays and travelling, special days and celebrations, assessment and closure, in the following weeks respectively. The control group had no intervention. Study data was collected by using an introductory information form and the Pittsburgh Sleep Quality Index (PSQI). Results: In our study, participants’ average age was 76.02 ± 7.31. 58.7% of them were male and 84.8% were single. All of them had at least one chronic disease. 76.1% did not need help for performing their daily life activities. The length of stay in the institution was 6.32 ± 3.85 years. According to the participants’ descriptive characteristics, there was no difference between groups. While there was no statistically significant difference between the pretest PSQI median scores (p > 0.05) of both groups, PSQI median score had a statistically significant decrease after 12 weeks of reminiscence therapy (p < 0.05). There was no statistically significant change in the median scores of the subcomponents of sleep latency, sleep duration, sleep efficiency, sleep disturbance and use of sleep medication before and after reminiscence therapy. After the 12-weeks reminiscence therapy, there was a statistically significant change in the median scores for the PSQI subcomponents of subjective sleep quality (p<0.05). Conclusion: Our study found that reminiscence therapy increased the sleep quality of the elderly living in nursing homes. Acknowledgment: This study (project no 2017-037) was supported by the Scientific Research Projects Coordination Unit of Aksaray University. We thank the elderly subjects for their kind participation.

Keywords: nursing, older people, reminiscence therapy, sleep

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Authors: Levent Basayigit, Sinan Demir, Yusuf Ucar, Burhan Kara

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Many research projects require accurate delineation of the different land cover type of the agricultural area. Especially it is critically important for the definition of specific plants like cannabis. However, the complexity of vegetation stands structure, abundant vegetation species, and the smooth transition between different seconder section stages make vegetation classification difficult when using traditional approaches such as the maximum likelihood classifier. Most of the time, classification distinguishes only between trees/annual or grain. It has been difficult to accurately determine the cannabis mixed with other plants. In this paper, a mixed distribution models approach is applied to classify pure and mix cannabis parcels using Worldview-2 imagery in the Lakes region of Turkey. Five different land use types (i.e. sunflower, maize, bare soil, and cannabis) were identified in the image. A constrained Gaussian mixture discriminant analysis (GMDA) was used to unmix the image. In the study, 255 reflectance ratios derived from spectral signatures of seven bands (Blue-Green-Yellow-Red-Rededge-NIR1-NIR2) were randomly arranged as 80% for training and 20% for test data. Gaussian mixed distribution model approach is proved to be an effective and convenient way to combine very high spatial resolution imagery for distinguishing cannabis vegetation. Based on the overall accuracies of the classification, the Gaussian mixed distribution model was found to be very successful to achieve image classification tasks. This approach is sensitive to capture the illegal cannabis planting areas in the large plain. This approach can also be used for monitoring and determination with spectral reflections in illegal cannabis planting areas.

Keywords: Gaussian mixture discriminant analysis, spectral mixture model, Worldview-2, land parcels

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Authors: Zanib Nafees, Amir Razaghizad, Ibtisam Mahmoud, Abhinav Sharma, Renzo Cecere

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More than one million surgeries are performed each year in Canada, resulting in more than 100,000 associated serious adverse events (SAEs) per year. These are defined as unintended injuries or complications that adversely affect the well-being of patients. In recent years, there has been a proliferation of digital health interventions that have the potential to assist, monitor, and educate patients—facilitating self-care following post-operative discharge. Among digital health, interventions are interactive-voice response technologies (IVRs), which have been shown to be highly effective in certain medical settings. Although numerous IVR-based interventions have been developed, their effectiveness and utility remain unclear, notably in post-operative settings. To the best of our knowledge, no systematic or scoping reviews have evaluated this topic to date. Thus, the objective of this scoping review protocol is to systematically map and explore the literature and evidence describing and examining IVR tools, implementation, evaluation, outcome, and experience for post-operative patients. The focus will be primarily on the evaluation of baseline performance status, clinical assessment, treatment outcomes, and patient management, including self-management and self-monitoring. The objective of this scoping review is to assess the extent of the literature to direct future research efforts by identifying gaps and limitations in the literature and to highlight relevant determinants of positive outcomes in the emerging field of IVR monitoring for health outcomes in post-operative patients.

Keywords: digital healthcare technologies, post-surgery, interactive voice technology, interactive voice response

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Authors: Ziyun Li, Jiudi Zhong, June Zhang

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Background: The diagnosis and treatment of lung cancer lead to varying degrees of psychological and social maladjustment among patients with lung cancer. Understanding psychosocial adjustment (PA) and its influencing factors in young and middle-aged lung cancer patients is essential to help them return to society and lead a normal life. Objectives: This study aims to examine the mediating role of resilience in the association between stigma and psychosocial adjustment among young and middle-aged patients with lung cancer. Methods: A total of 235 patients with lung cancer were recruited from a tertiary grade A cancer center in southern China and investigated using a self-designed general information questionnaire, Psychosocial Adjustment to Illness Scale Self-Report, Social Impact Scale, and Conner-Davidson Resilience Scale. Results: The mean score of PA was (32.61±14.75), and its influencing factors included treatment modalities, stigma, and resilience. The total effect of stigma on PA was significant (total effect=0.418, SE=0.045, 95%CI [0.310-0.497]), and a positive indirect effect was identified for stigma on PA via resilience (indirect effect=0.143, SE=0.041, 95% CI [0.075-0.236]). Conclusion: Stigma and resilience are significantly associated with PA, and resilience is also a mediating variable between stigma and PA. This study suggests that individualized interventions can be made to improve the PA by alleviating their stigma, or by enhancing their resilience in young and middle-aged lung cancer patients.

Keywords: psychosocial adjustment, lung cancer, cancer caring, nursing, young and middle-aged

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Authors: Huang Hao, Li Weiwen

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Global biodiversity continues to decline as a result of global climate change and various human activities, such as habitat destruction, pollution, introduction of alien species and overfishing. Although there are connections between global marine organisms more or less, it is better to have clear geographical boundaries in order to facilitate the assessment and management of different biogeographical zones. And so area based management tools (ABMT) are considered as the most effective means for the conservation and sustainable use of marine biodiversity. On a large scale, the geographical gap (or barrier) is the main factor to influence the connectivity, diffusion, ecological and evolutionary process of marine organisms, which results in different distribution patterns. On a small scale, these factors include geographical location, geology, and geomorphology, water depth, current, temperature, salinity, etc. Therefore, the analysis on geographic and environmental factors is of great significance in the study of biodiversity characteristics. This paper summarizes the marine spatial classification and ABMTs used in coastal area, open oceans and deep sea. And analysis principles and methods of marine spatial classification based on biogeographic related factors, and take China Near Sea (CNS) area as case study, and select key biogeographic related factors, carry out marine spatial classification at biological region scale, ecological regionals scale and biogeographical scale. The research shows that CNS is divided into 5 biological regions by climate and geographical differences, the Yellow Sea, the Bohai Sea, the East China Sea, the Taiwan Straits, and the South China Sea. And the bioregions are then divided into 12 ecological regions according to the typical ecological and administrative factors, and finally the eco-regions are divided into 98 biogeographical units according to the benthic substrate types, depth, coastal types, water temperature, and salinity, given the integrity of biological and ecological process, the area of the biogeographical units is not less than 1,000 km². This research is of great use to the coastal management and biodiversity conservation for local and central government, and provide important scientific support for future spatial planning and management of coastal waters and sustainable use of marine biodiversity.

Keywords: spatial classification, marine biodiversity, bio-geographical, conservation

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Authors: Hanene Sahli, Aymen Mouelhi, Marwa Hajji, Amine Ben Slama, Mounir Sayadi, Farhat Fnaiech, Radhwane Rachdi

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Fetal abnormality is still a public health problem of interest to both mother and baby. Head defect is one of the most high-risk fetal deformities. Fetal head categorization is a sensitive task that needs a massive attention from neurological experts. In this sense, biometrical measurements can be extracted by gynecologist doctors and compared with ground truth charts to identify normal or abnormal growth. The fetal head biometric measurements such as Biparietal Diameter (BPD), Occipito-Frontal Diameter (OFD) and Head Circumference (HC) needs to be monitored, and expert should carry out its manual delineations. This work proposes a new approach to automatically compute BPD, OFD and HC based on morphological characteristics extracted from head shape. Hence, the studied data selected at the same Gestational Age (GA) from the fetal Ultrasound images (US) are classified into two categories: Normal and abnormal. The abnormal subjects include hydrocephalus, microcephaly and dolichocephaly anomalies. By the use of a support vector machines (SVM) method, this study achieved high classification for automated detection of anomalies. The proposed method is promising although it doesn't need expert interventions.

Keywords: biometric measurements, fetal head malformations, machine learning methods, US images

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Authors: Knol Hester, Müller Swantje, Danchenko Natalya

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Objective: Falls in older people cause an autonomy loss and result in an economic burden. LCare is an AI-based application to manage fall risks. The study's aim was to assess the effect of LCare use on patient outcomes in nursing homes in Germany. Methods: LCare identifies and monitors fall risks through a 3D-gait analysis and a digital questionnaire, resulting in tailored recommendations on fall prevention. A study was conducted with AOK Baden-Württemberg (01.09.2019- 31.05.2021) in 16 care facilities. Assessments at baseline and follow-up included: a fall risk score; falls (baseline: fall history in the past 12 months; follow-up: a fall record since the last analysis); fall-related injuries and hospitalizations; gait speed; fear of falling; psychological stress; nurses experience on app use. Results: 94 seniors were aged 65-99 years at the initial analysis (average 84±7 years); 566 mobility analyses were carried out in total. On average, the fall risk was reduced by 17.8 % as compared to the baseline (p<0.05). The risk of falling decreased across all subgroups, including a trend in dementia patients (p=0.06), constituting 43% of analyzed patients, and patients with walking aids (p<0.05), constituting 76% of analyzed patients. There was a trend (p<0.1) towards fewer falls and fall-related injuries and hospitalizations (baseline: 23 seniors who fell, 13 injury consequences, 9 hospitalizations; follow-up: 14 seniors who fell, 2 injury consequences, 0 hospitalizations). There was a 16% improvement in gait speed (p<0.05). Residents reported less fear of falling and psychological stress by 38% in both outcomes (p<0.05). 81% of nurses found LCare effective. Conclusions: In the presented study, the use of LCare app was associated with a reduction of fall risk among nursing home residents, improvement of health-related outcomes, and a trend toward reduction in injuries and hospitalizations. LCare may help to improve senior resident care and save healthcare costs.

Keywords: falls, digital healthcare, falls prevention, nursing homes, seniors, AI, digital assessment

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Authors: Hyung Jun Ahn

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With the growing importance of social media, it is imperative to analyze it to understand the users. Users share useful information and their experience through social media, where much of what is shared is in the form of texts. This study focused on blogs and aimed to test whether the psycho-linguistic characteristics of blog texts vary with the subject or the type of experience of the texts. For this goal, blog texts about four different types of experience, Go, skiing, reading, and musical were collected through the search API of the Tistory blog service. The analysis of the texts showed that various psycholinguistic characteristics of the texts are different across the four categories of the texts. Moreover, the machine learning experiment using the characteristics for automatic text classification showed significant performance. Specifically, the ensemble method, based on functional tree and bagging appeared to be most effective in classification.

Keywords: blog, social media, text analysis, psycholinguistics

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Authors: Rujia Chen, Ajit Narayanan

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Convolutional neural networks (CNN), as typically applied in deep learning, use layer-wise backpropagation (BP) to construct filters and kernels for feature extraction. Such filters are 2D or 3D groups of weights for constructing feature maps at subsequent layers of the CNN and are shared across the entire input. BP as a gradient descent algorithm has well-known problems of getting stuck at local optima. The use of genetic algorithms (GAs) for evolving weights between layers of standard artificial neural networks (ANNs) is a well-established area of neuroevolution. In particular, the use of crossover techniques when optimizing weights can help to overcome problems of local optima. However, the application of GAs for evolving the weights of filters and kernels in CNNs is not yet an established area of neuroevolution. In this paper, a GA-based filter development algorithm is proposed. The results of the proof-of-concept experiments described in this paper show the proposed GA algorithm can find filter weights through evolutionary techniques rather than BP learning. For some simple classification tasks like geometric shape recognition, the proposed algorithm can achieve 100% accuracy. The results for MNIST classification, while not as good as possible through standard filter learning through BP, show that filter and kernel evolution warrants further investigation as a new subarea of neuroevolution for deep architectures.

Keywords: neuroevolution, convolutional neural network, genetic algorithm, filters, kernels

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Authors: Abewardhana Arachchi Bandula Dimuthu Priyadarshana Abewardhana, Rasika Nirosh Gonapinuwala Vithanage, Karawe Thanthreege Amila Madusanka Perera, Asanka Sanjeewa Karunarathne, Navullage Mayuri Radhika Perera

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The phenomenon of irregular migration and human trafficking presents multifaceted challenges to Sri Lanka, with specific focus on the migration routes to the United Arab Emirates (UAE), the Sultanate of Oman, and Malaysia. This research critically assesses the efficacy of a pilot project instituted at Bandaranaike International Airport aimed at the identification and deterrence of potential irregular migrants. Additionally, the study conducts a nuanced analysis of the geographical tendencies pertaining to passengers who revise their migration intentions at the airport. Pertinently, the findings indicate that Colombo and Gampaha Districts emerge as the most susceptible to human trafficking, with Galle, Nuwaraeliya, Rathnapura, and Polonnaruwa Districts following as areas of elevated concern, particularly within the framework of the 'visit visa' scenario. These insights emanate from an extensive data collection period spanning 50 days of the pilot project, encompassing 1,479 passengers, of which 46 returnees reported to the Safe Migration Promotion Unit. The research is founded on the twin objectives of comprehending the motivations of passengers and evaluating the effectiveness of interventions, with a view to devising precision-targeted prevention strategies. Through this endeavor, the study actively contributes to the safeguarding of the rights and welfare of migrants, significantly advancing the ongoing battle against irregular migration.

Keywords: irregular migration, human trafficking, airport interventions, geographic trends

Procedia PDF Downloads 80