Search results for: multiclass support vector machines

Authors: Sadia Basar, Naila Habib, Awais Adnan

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The paper presents the Technique for Image Segmentation Using K-Means Clustering Classification. The presented algorithms were specific, however, missed the neighboring information and required high-speed computerized machines to run the segmentation algorithms. Clustering is the process of partitioning a group of data points into a small number of clusters. The proposed method is content-aware and feature extraction method which is able to run on low-end computerized machines, simple algorithm, required low-quality streaming, efficient and used for security purpose. It has the capability to highlight the boundary and the object. At first, the user enters the data in the representation of the input. Then in the next step, the digital image is converted into groups clusters. Clusters are divided into many regions. The same categories with same features of clusters are assembled within a group and different clusters are placed in other groups. Finally, the clusters are combined with respect to similar features and then represented in the form of segments. The clustered image depicts the clear representation of the digital image in order to highlight the regions and boundaries of the image. At last, the final image is presented in the form of segments. All colors of the image are separated in clusters.

Keywords: clustering, image segmentation, K-means function, local and global minimum, region

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Authors: Sarita Agarwal, Deepika Delsa Dean

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Like the task of transferring photo images to artistic images, image-to-image translation aims to translate the data to the imitated data which belongs to the target domain. Neural Style Transfer and CycleGAN are two well-known deep learning architectures used for photo image-to-art image transfer. However, studies involving these two models concentrate on one-to-one domain translation, not one-to-multi domains translation. Our study tries to investigate deep learning architectures, which can be controlled to yield multiple artistic style translation only by adding a conditional vector. We have expanded CycleGAN and constructed Conditional CycleGAN for 5 kinds of categories translation. Our study found that the architecture inserting conditional vector into the middle layer of the Generator could output multiple artistic images.

Keywords: genetic counseling, FMR1 gene, fragile x-associated primary ovarian insufficiency, premutation

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Authors: Yonguk L. Park, Jeonghoon Seol

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The Republic of Korea is facing a special situation as it is the only divided country in the world. Even though Korea is facing such unstable circumstances in terms of a foreign diplomacy situation, Korea is one of the countries who, in concern for world peace, have been sending troops overseas. The troops spend more than a year at sea and may suffer from different types of psychological disorders. The purpose of this study is to try to find factors that promote psychological well-being of troops and improve their psychological health. We investigated the effect of dispatch sailors’ occupational calling and social support on anxiety before they are sent overseas and also examined the interaction between occupational calling and social support on anxiety. One hundred thirty-eight dispatched sailors participated in this study, wherein they completed the Korean calling scale, multifaceted social support scale, and anxiety scale –Y form. We analyzed the data using hierarchical regression. The results showed that after controlling gender, marital status, and the previous experiences of dispatch, those who have a higher level of occupational calling and perceived social support experienced a low level of anxiety before they are sent (β = -.276, β = -.395). Furthermore, we examined the interaction effect. If the troops’ perceived social support is high, they experience a low level of anxiety—even if they have a low level of occupational calling. This study confirms that both occupational calling and social support reduce the level of anxiety of the troops. The research provides meaningful information in understanding those who serve in the Navy’s distinctive situations and contributes to improving their psychological well-being. We suggest that sailors undergo training to have a higher occupational calling and healthy relationships with friends, families, and co-workers who provide emotional and social support.

Keywords: navy, occupational calling, social support, anxiety

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Authors: Fariba Sepahvand, Seyed Hassan Moosa-kazemi

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The objective was to determine the population dynamics of Anopheles maculipinnis s.l. in relation to probable malaria transmission. The study was carried out in three villages in Isfahan and charmahal bakhteari provinces of Iran, from April to March 2014. Mosquitoes were collected by Total catch, Human and Animal bait collection. An. maculipinnis play as a dominant vector with exophagic and endophilic behavior. Ovary dissection revealed four dilatations indicate at least 9% of the population can reach to the dangerous age to potentially malaria transmission. Two peaks of blood feeding were observed, 9.00-10.00 P.M, and the 12.00-00.01 A.M. The gonotrophic cycle, survival rate, life expectancy of the species was 4, 0.82 and five days, respectively. Vectorial capacity was measured as 0.028. In conclusion, moderate climatic conditions support the persistence, density and longevity of An maculipinnis s.l. could result in more significant malaria transmission.

Keywords: age determination, Anopheles maculipinnis, center of Iran, Malaria

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Authors: Pei-Jyun Hsieh, Cheng-Hsuan Li, Bor-Chen Kuo

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For hyperspectral image classification, feature reduction is an important pre-processing for avoiding the Hughes phenomena due to the difficulty for collecting training samples. Hence, lots of researches developed feature selection methods such as F-score, HSIC (Hilbert-Schmidt Independence Criterion), and etc., to improve hyperspectral image classification. However, most of them only consider the class separability in the original space, i.e., a linear class separability. In this study, we proposed a nonlinear class separability measure based on kernel trick for selecting an appropriate feature subset. The proposed nonlinear class separability was formed by a generalized RBF kernel with different bandwidths with respect to different features. Moreover, it considered the within-class separability and the between-class separability. A genetic algorithm was applied to tune these bandwidths such that the smallest with-class separability and the largest between-class separability simultaneously. This indicates the corresponding feature space is more suitable for classification. In addition, the corresponding nonlinear classification boundary can separate classes very well. These optimal bandwidths also show the importance of bands for hyperspectral image classification. The reciprocals of these bandwidths can be viewed as weights of bands. The smaller bandwidth, the larger weight of the band, and the more importance for classification. Hence, the descending order of the reciprocals of the bands gives an order for selecting the appropriate feature subsets. In the experiments, three hyperspectral image data sets, the Indian Pine Site data set, the PAVIA data set, and the Salinas A data set, were used to demonstrate the selected feature subsets by the proposed nonlinear feature selection method are more appropriate for hyperspectral image classification. Only ten percent of samples were randomly selected to form the training dataset. All non-background samples were used to form the testing dataset. The support vector machine was applied to classify these testing samples based on selected feature subsets. According to the experiments on the Indian Pine Site data set with 220 bands, the highest accuracies by applying the proposed method, F-score, and HSIC are 0.8795, 0.8795, and 0.87404, respectively. However, the proposed method selects 158 features. F-score and HSIC select 168 features and 217 features, respectively. Moreover, the classification accuracies increase dramatically only using first few features. The classification accuracies with respect to feature subsets of 10 features, 20 features, 50 features, and 110 features are 0.69587, 0.7348, 0.79217, and 0.84164, respectively. Furthermore, only using half selected features (110 features) of the proposed method, the corresponding classification accuracy (0.84168) is approximate to the highest classification accuracy, 0.8795. For other two hyperspectral image data sets, the PAVIA data set and Salinas A data set, we can obtain the similar results. These results illustrate our proposed method can efficiently find feature subsets to improve hyperspectral image classification. One can apply the proposed method to determine the suitable feature subset first according to specific purposes. Then researchers can only use the corresponding sensors to obtain the hyperspectral image and classify the samples. This can not only improve the classification performance but also reduce the cost for obtaining hyperspectral images.

Keywords: hyperspectral image classification, nonlinear feature selection, kernel trick, support vector machine

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Authors: Cristina Galván-Fernández, Elena Barberà, Jingjing Zhang

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In recent years, MOOCs have been in the spotlight for its high drop-out rates, which potentially impact on the quality of the learning experience. This study attempts to explore how learning support can be used to keep student retention, and in turn to improve the quality of learning in MOOCs. In this study, the patterns of learning support were identified from a total of 4202592 units of video sessions, clickstream data of 25600 students, and 382 threads generated in 10 forums (optional and mandatory) in five different types of MOOCs (e.g. conventional MOOCs, professional MOOCs, and informal MOOCs). The results of this study have shown a clear correlation between the types of MOOCs, the design framework of the MOOCs, and the learning support. The patterns of tutor-peer interaction are identified, and are found to be highly correlated with student retention in all five types of MOOCs. In addition, different patterns of ‘good’ students were identified, which could potentially inform the instruction design of MOOCs.

Keywords: higher education, learning support, MOOC, retention

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Authors: Daohong Yang, Xiang Zhang, Lei Li, Wanting Zhou

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Visual Simultaneous Localization and Mapping (VSLAM) is a technology that obtains information in the environment for self-positioning and mapping. It is widely used in computer vision, robotics and other fields. Many visual SLAM systems, such as OBSLAM3, employ a constant-speed motion model that provides the initial pose of the current frame to improve the speed and accuracy of feature matching. However, in actual situations, the constant velocity motion model is often difficult to be satisfied, which may lead to a large deviation between the obtained initial pose and the real value, and may lead to errors in nonlinear optimization results. Therefore, this paper proposed a motion model based on acceleration, which can be applied on most SLAM systems. In order to better describe the acceleration of the camera pose, we decoupled the pose transformation matrix, and calculated the rotation matrix and the translation vector respectively, where the rotation matrix is represented by rotation vector. We assume that, in a short period of time, the changes of rotating angular velocity and translation vector remain the same. Based on this assumption, the initial pose of the current frame is estimated. In addition, the error of constant velocity model was analyzed theoretically. Finally, we applied our proposed approach to the ORBSLAM3 system and evaluated two sets of sequences on the TUM dataset. The results showed that our proposed method had a more accurate initial pose estimation and the accuracy of ORBSLAM3 system is improved by 6.61% and 6.46% respectively on the two test sequences.

Keywords: error estimation, constant acceleration motion model, pose estimation, visual SLAM

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Authors: R. R. Dewi Rahmawaty Aktyani Putri, Earmporn Thongkrajai, Dedy Purwito

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There were many instruments have been developed to assess social support which has the different dimension in breast cancer patients. The Issue of measurement is a challenge to determining the component of dimensional concept, defining the unit of measurement, and establishing the validity and reliability of the measurement. However, the instruments where need to know how much support which obtained and perceived among women with breast cancer who undergone chemotherapy which it can help nurses to prevent of non-adherence in chemotherapy. This study aimed to measure the reliability of BC-SSAS instrument among 30 Indonesian women with breast cancer aged 18 years and above who undergone chemotherapy for six cycles in the oncological unit of Outpatient Department (OPD), Margono Soekardjo Hospital, Central Java, Indonesia. Data were collected during October to December 2015 by using modified the Breast Cancer Social Support Assessment (BC-SSAS). The Cronbach’s alpha analysis was carried out to measure internal consistency for reliability test of BC-SSAS instrument. This study used five experts for content validity index. The results showed that for content validity, I-CVI was 0.98 and S-CVI was 0.98; Cronbach’s alpha value was 0.971 and the Cronbach’s alpha coefficients for the subscales were high, with 0.903 for emotional support, 0.865 for informational support, 0.901 for tangible support, 0.897 for appraisal support and 0.884 for positive interaction support. The results confirmed that the BC-SSAS instrument has high reliability. BC-SSAS instruments were reliable and can be used in health care services to measure the social support received and perceived among women with breast cancer who undergone chemotherapy so that preventive interventions can be developed and the quality of health services can be improved.

Keywords: BC-SSAS, women with breast cancer, chemotherapy, Indonesia

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Authors: Shauma L. Tamba

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This study examined whether morality is the most important aspect in actual behavior. The prediction was that people tend to behave in line with moral (as compared to competence) norms, especially when such norms are presented by their ingroup. The actual behavior that was tested was support for a military intervention without a mandate from the UN. In addition, this study also examined whether identification with the ingroup and religion moderated the effect of group and norm on support for the norm that was prescribed by their ingroup. The prediction was that those who identified themselves higher with the ingroup moral would show a higher support for the norm. Furthermore, the prediction was also that those who have religion would show a higher support for the norm in the ingroup moral rather than competence. In an online survey, participants were asked to read a scenario in which a military intervention without a mandate was framed as either the moral (but stupid) or smart (but immoral) thing to do by members of their own (ingroup) or another (outgroup) society. This study found that when people identified themselves with the smart (but immoral) norm, they showed a higher support for the norm. However, when people identified themselves with the moral (but stupid) norm, they tend to show a lesser support towards the norm. Most of the results in the study did not support the predictions. Possible explanations and implications are discussed.

Keywords: morality, competence, ingroup identification, religion, group norm

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Authors: Ahmad Zulfahmi Mohd Kamaruzaman, Mohd Ismail Ibrahim, Ariffin Marzuki Mokhtar, Maizun Mohd Zain, Saiful Nazri Satiman, Mohd Najib Majdi Yaacob

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Background: Aftermath any patient safety incidents, the involved healthcare providers possibly sustained second victim-related distress (second victim distress and reduced their professional efficacy), with subsequent negative work-related outcomes or vice versa cultivating resilience. This study aimed to investigate the factors affecting negative work-related outcomes and resilience, with the triad of support; colleague, supervisor, and institutional support as the hypothetical mediators. Methods: This was a cross sectional study recruiting a total of 733 healthcare providers from three tertiary care in Kelantan, Malaysia. Three steps of hierarchical linear regression were developed for each outcome; negative work-related outcomes and resilience. Then, four multiple mediator models of support triad were analyzed. Results: Second victim distress, professional efficacy, and the support triad contributed significantly for each regression model. In the pathway of professional efficacy on each negative work-related outcomes and resilience, colleague support partially mediated the relationship. As for second victim distress on negative work related outcomes, colleague and supervisor support were the partial mediator, and on resilience; all support triad also produced a similar effect. Conclusion: Second victim distress, professional efficacy, and the support triad influenced the relationship with the negative work-related outcomes and resilience. Support triad as the mediators ameliorated the effect in between and explained the urgency of having good support for recovery post encountering patient safety incidents.

Keywords: second victims, patient safety incidents, hierarchical linear regression, mediation, support

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Authors: Iman A. Saad, Loay E. George

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Iris pattern is an important biological feature of human body; it becomes very hot topic in both research and practical applications. In this paper, an algorithm is proposed for iris recognition and a simple, efficient and fast method is introduced to extract a set of discriminatory features using first order gradient operator applied on grayscale images. The gradient based features are robust, up to certain extents, against the variations may occur in contrast or brightness of iris image samples; the variations are mostly occur due lightening differences and camera changes. At first, the iris region is located, after that it is remapped to a rectangular area of size 360x60 pixels. Also, a new method is proposed for detecting eyelash and eyelid points; it depends on making image statistical analysis, to mark the eyelash and eyelid as a noise points. In order to cover the features localization (variation), the rectangular iris image is partitioned into N overlapped sub-images (blocks); then from each block a set of different average directional gradient densities values is calculated to be used as texture features vector. The applied gradient operators are taken along the horizontal, vertical and diagonal directions. The low order norms of gradient components were used to establish the feature vector. Euclidean distance based classifier was used as a matching metric for determining the degree of similarity between the features vector extracted from the tested iris image and template features vectors stored in the database. Experimental tests were performed using 2639 iris images from CASIA V4-Interival database, the attained recognition accuracy has reached up to 99.92%.

Keywords: iris recognition, contrast stretching, gradient features, texture features, Euclidean metric

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Authors: Lotha Valan, Åsa Hörnsten, Ulf Isaksson

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Introduction: Sweden has a national child health care program (CHCP) where all parents are offered support to raise their children and support them for lifelong health. A systematic review concludes that there is a request for guidance in using the internet effectively for the health purposes of their children. However, a study about internet use among young mothers means that the internet is not always easy to navigate for parents, and they may need support. To fill this gap and develop a digital channel to complement the child health care (CHC) for the support of parents of children within CHC, there is a demand to investigate parents' needs in relation to this purpose. Methods: The study had a qualitative approach using focus group interviews with parents. The interview data were analyzed using qualitative content analysis. Results: The main theme highlights that parents expected that a digital support channel would be something that might strengthen them toward independence concerning the care of their children in a positive way. However, they also felt that they needed personal support and that relationships with other parents and the child health care nurse were significant and meaningful. Another parental desire that emerged was that a future digital channel would facilitate and simplify access to care, and they suggested having both planned and urgent times available for parents to book. The digital channel was expected to make this possible and be a good complement to the physical contacts the traditional child healthcare currently offers. Discussion/conclusions: The parents in this study believed that digital solutions could increase their parental power in relation to the care of their children. Examples were given as nurse-led parent groups where parents with similar problems and experiences around their children could support each other and were expected to strengthen them over time. The parents stressed that a planned digital support channel also needs satisfactory solutions for both contact and response. It was suggested that there should be bookable times for both planned and urgent needs and also the possibility of rescheduling visits.

Keywords: child healthcare, parents, digital support, nursing

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Authors: D. Pugazhenthi, B. Sree Vidya

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Cloud computing is one of the emerging technologies that enables end users to use the services of cloud on ‘pay per usage’ strategy. This technology grows in a fast pace and so is its security threat. One among the various services provided by cloud is storage. In this service, security plays a vital factor for both authenticating legitimate users and protection of information. This paper brings in efficient ways of authenticating users as well as securing information on the cloud. Initial phase proposed in this paper deals with an authentication technique using multi-factor and multi-dimensional authentication system with multi-level security. Unique identification and slow intrusive formulates an advanced reliability on user-behaviour based biometrics than conventional means of password authentication. By biometric systems, the accounts are accessed only by a legitimate user and not by a nonentity. The biometric templates employed here do not include single trait but multiple, viz., iris and finger prints. The coordinating stage of the authentication system functions on Ensemble Support Vector Machine (SVM) and optimization by assembling weights of base SVMs for SVM ensemble after individual SVM of ensemble is trained by the Artificial Fish Swarm Algorithm (AFSA). Thus it helps in generating a user-specific secure cryptographic key of the multimodal biometric template by fusion process. Data security problem is averted and enhanced security architecture is proposed using encryption and decryption system with double key cryptography based on Fuzzy Neural Network (FNN) for data storing and retrieval in cloud computing . The proposing scheme aims to protect the records from hackers by arresting the breaking of cipher text to original text. This improves the authentication performance that the proposed double cryptographic key scheme is capable of providing better user authentication and better security which distinguish between the genuine and fake users. Thus, there are three important modules in this proposed work such as 1) Feature extraction, 2) Multimodal biometric template generation and 3) Cryptographic key generation. The extraction of the feature and texture properties from the respective fingerprint and iris images has been done initially. Finally, with the help of fuzzy neural network and symmetric cryptography algorithm, the technique of double key encryption technique has been developed. As the proposed approach is based on neural networks, it has the advantage of not being decrypted by the hacker even though the data were hacked already. The results prove that authentication process is optimal and stored information is secured.

Keywords: artificial fish swarm algorithm (AFSA), biometric authentication, decryption, encryption, fingerprint, fusion, fuzzy neural network (FNN), iris, multi-modal, support vector machine classification

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Authors: Uyi Kizito Ehigiamusoe

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The paper examines the impact of money market on economic growth in Nigeria using data for the period 1980-2012. Econometrics techniques such as Ordinary Least Squares Method, Johanson’s Co-integration Test and Vector Error Correction Model were used to examine both the long-run and short-run relationship. Evidence from the study suggest that though a long-run relationship exists between money market and economic growth, but the present state of the Nigerian money market is significantly and negatively related to economic growth. The link between the money market and the real sector of the economy remains very weak. This implies that the market is not yet developed enough to produce the needed growth that will propel the Nigerian economy because of several challenges. It was therefore recommended that government should create the appropriate macroeconomic policies, legal framework and sustain the present reforms with a view to developing the market so as to promote productive activities, investments, and ultimately economic growth.

Keywords: economic growth, investments, money market, money market challenges, money market instruments

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Authors: Salim Ouchtati, Aissa Belmeguenai, Mouldi Bedda

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In this study, we propose an offline system for the recognition of the fragmented handwritten numeric chains. Firstly, we realized a recognition system of the isolated handwritten digits, in this part; the study is based mainly on the evaluation of neural network performances, trained with the gradient backpropagation algorithm. The used parameters to form the input vector of the neural network are extracted from the binary images of the isolated handwritten digit by several methods: the distribution sequence, sondes application, the Barr features, and the centered moments of the different projections and profiles. Secondly, the study is extended for the reading of the fragmented handwritten numeric chains constituted of a variable number of digits. The vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits).

Keywords: features extraction, handwritten numeric chains, image processing, neural networks

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Authors: Amir Rashid

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Cloud Computing frameworks empower organizations to cut expenses by outsourcing computation resources on-request. As of now, customers of Cloud service providers have no methods for confirming the privacy and ownership of their information and data. To address this issue we propose the platform of a trusted cloud computing program (TCCP). TCCP empowers Infrastructure as a Service (IaaS) suppliers, for example, Amazon EC2 to give a shout box execution condition that ensures secret execution of visitor virtual machines. Also, it permits clients to bear witness to the IaaS supplier and decide if the administration is secure before they dispatch their virtual machines. This paper proposes a Trusted Cloud Computing Platform (TCCP) for guaranteeing the privacy and trustworthiness of computed data that are outsourced to IaaS service providers. The TCCP gives the deliberation of a shut box execution condition for a client's VM, ensuring that no cloud supplier's authorized manager can examine or mess up with its data. Furthermore, before launching the VM, the TCCP permits a client to dependably and remotely acknowledge that the provider at backend is running a confided in TCCP. This capacity extends the verification of whole administration, and hence permits a client to confirm the data operation in secure mode.

Keywords: cloud security, IaaS, cloud data privacy and integrity, hybrid cloud

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Authors: Abdulla D. Alblooshi

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The stock market is a representation of human behavior and psychology, such as fear, greed, and discipline. Those are manifested in the form of price movements during the trading sessions. Therefore, predicting the stock movement and prices is a challenging effort. However, those trading sessions produce a large amount of data that can be utilized to train an AI agent for the purpose of predicting the stock movement. Predicting the stock market price action will be advantageous. In this paper, the stock movement data of three DFM listed stocks are studied using historical price movements and technical indicators value and used to train an agent using KNN and SVM methods to predict the future price movement. MATLAB Toolbox and a simple script is written to process and classify the information and output the prediction. It will also compare the different learning methods and parameters s using metrics like RMSE, MAE, and R².

Keywords: KNN, ANN, style, SVM, stocks, technical indicators, RSI, MACD, moving averages, RMSE, MAE

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Authors: Rosemary E. Huntriss, Lucy Jones

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Obesity and excess weight continue to be significant health problems in England. Traditional weight management programmes offer face-to-face support or group education. Remote care is recognised as a viable means of support; however, its effectiveness has not previously been evaluated in a tier 3 weight management setting. This service evaluation explored the effectiveness of online coaching, telephone support, and face-to-face support as optional management strategies within a tier 3 weight management programme. Outcome data were collected for adults with a BMI ≥ 45 or ≥ 40 with complex comorbidity who were referred to a Tier 3 weight management programme from January 2018 and had been discharged before October 2018. Following an initial 45-minute consultation with a specialist weight management dietitian, patients were offered a choice of follow-up support in the form of online coaching supported by an app (8 x 15 minutes coaching), face-to-face or telephone appointments (4 x 30 minutes). All patients were invited to a final 30-minute face-to-face assessment. The planned intervention time was between 12 and 24 weeks. Patients were offered access to adjunct face-to-face or telephone psychological support. One hundred and thirty-nine patients were referred into the programme from January 2018 and discharged before October 2018. One hundred and twenty-four patients (89%) attended their initial assessment. Out of those who attended their initial assessment, 110 patients (88.0%) completed more than half of the programme and 77 patients (61.6%) completed all sessions. The average length of the completed programme (all sessions) was 17.2 (SD 4.2) weeks. Eighty-five (68.5%) patients were coached online, 28 (22.6%) patients were supported face-to-face support, and 11 (8.9%) chose telephone support. Two patients changed from online coaching to face-to-face support due to personal preference and were included in the face-to-face group for analysis. For those with data available (n=106), average weight loss across the programme was 4.85 (SD 3.49)%; average weight loss was 4.70 (SD 3.19)% for online coaching, 4.83 (SD 4.13)% for face-to-face support, and 6.28 (SD 4.15)% for telephone support. There was no significant difference between weight loss achieved with face-to-face vs. online coaching (4.83 (SD 4.13)% vs 4.70 (SD 3.19) (p=0.87) or face-to-face vs. remote support (online coaching and telephone support combined) (4.83 (SD 4.13)% vs 4.85 (SD 3.30)%) (p=0.98). Remote support has been shown to be as effective as face-to-face support provided by a dietitian in the short-term within a tier 3 weight management setting. The completion rates were high compared with another tier 3 weight management services suggesting that offering remote support as an option may improve completion rates within a weight management service.

Keywords: dietitian, digital health, obesity, weight management

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Authors: Hamna Hamid, Kashmala Zaman

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The aim of this study was to evaluate the relationship between self-efficacy, resilience and social support among clinical psychologists, counselors and teachers of special schools. The study also assesses the gender differences on self-efficacy, resilience, social support and vicarious trauma and also vicarious trauma differences among three professions i.e. clinical psychologists, counselors and teachers of special schools. A sample of 150 women and 97 men were handed out a set questionnaire to complete: General Self-Efficacy Scale, Brief Resilience Scale, Multidimensional Scale of Perceived Social Support and Vicarious Trauma Scale. Results showed that there is significant negative correlation between self-efficacy, resilience and vicarious trauma. Women experiences higher levels of vicarious trauma as compared to men. While clinical psychologists and counselors experience higher levels of vicarious trauma as compared to teachers of special schools. Moderation effect of social support is not significant towards resilience and vicarious trauma.

Keywords: self-efficacy, resilience, vicarious trauma, social-support

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Authors: Subagyo Yotopranoto, Sri Wijayanti Sulistyawati, Sukmawati Basuki, Budi Armika, Yoes Prijatna Dachlan

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Malaria is a parasitic infectious disease that still remains a health problem in the world, including Indonesia. There is an outbreak happen at West Nusa Tenggara in 2007. A tourist spot in West Nusa Tenggara called West Lombok is mesoendemic area for malaria. Tanjung is the highest malaria morbidity subdistrict in West Lombok. Thus, the research conducted for the presence of a new species of malaria vectors, that are suspected of one factors which caused high morbidity of malaria in this region. The study was conducted in coastal and highland areas. We collected and identified Anopheles larvae from their breeding places. We also collected and identified Anopheles adult mosquitoes with outdoor cow net, indoor and outdoor human bait. In coastal area (Tembobor village), we found Anopheles vagus larvae from rivers as its breeding places. In highland area (Dasan Tengah village), we found An. subpictus from pool, lagoon, and river as its breeding places. In coastal area, with outdoor human bait, we collected An. vagus and An. subpictus adult mosquitoes. With indoor human bait, we collected An. subpictus adult mosquitoes. Whereas with outdoor cow net, we collected An. subpictus and An. maculatus, the first was more dominant. Furthermore, An subpictus strong suspected as malaria vector in coastal area. Anopheles subpictus was an anthropozoophylic mosquitoes, because it was found at indoor and outdoor places.

Keywords: malaria, vector, Tanjung, West Nusa Tenggara

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Authors: Emily J. Winnall

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It is understood that China has the largest population of people living with dementia in the world; however, little is known about this culturally diverse community, specifically the Chinese Communities, which has been poorly represented in past British research Literature. Further research is needed to gain a greater understanding of the support needs of caregivers caring for a relative living with dementia from the Chinese background. Dementia care and caregivers in Chinese communities are less investigated. The study is a case study of two Chinese community centers and one housing support service. Semi-structured one-to-one interviews and a pilot questionnaire were used as the methods for the study. A toolkit will also be created as a document that provides guidance and signposting to health and social care services for Chinese communities. The findings identified three main themes. Caregivers do not receive any formal support from the UK health and social services, and they felt they would have benefited from getting advice on what support they could access. Furthermore, the data also identified that Chinese organisations do not have the knowledge of dementia, to be able to support those living with dementia and their families. Also, people living with dementia and their families rarely present to Chinese organisations and UK health and social care services, meaning they are not receiving the support they are entitled to or need. Additionally, the community center would like to see workshops/courses around dementia for people from Chinese backgrounds. The study concludes that people from Chinese cultural backgrounds do not have sufficient access to support from UK health and social care services. More information needs to be published that will benefit Chinese communities.

Keywords: Chinese, Chinese organisations, Dementia, family caregivers, social care

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Authors: T. Yamazaki, R. Onuma, H. Kaminaga, Y. Miyadera, S. Nakamura

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Web exploration has increasingly diversified in accordance with the development of browsing environments on the Internet. Moreover, advanced exploration often conducted in intellectual activities such as surveys in research activities. This kind of exploration is conducted for a long period with trials and errors. In such a case, it is extremely important for a user to accumulate the search contexts and understand them. However, existing support systems were not effective enough since most systems could not handle the various factors involved in the exploration. This research aims to develop a novel system to support web exploration focusing on the accumulation of the search contexts. This paper mainly describes the outline of the system. An experiment using the system is also described. Finally, features of the system are discussed based on the results.

Keywords: web exploration context, refinement of search intention, accumulation of context, exploration support, information visualization

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Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

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Authors: Diego Santibañez Oyarce, Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

Abstract:

The advent of high-throughput sequencing technologies has revolutionized genomics, generating vast amounts of genetic data that challenge traditional bioinformatics methods. Machine learning addresses these challenges by leveraging computational power to identify patterns and extract information from large datasets. However, biological sequence data, being symbolic and non-numeric, must be converted into numerical formats for machine learning algorithms to process effectively. So far, some encoding methods, such as one-hot encoding or k-mers, have been explored. This work proposes additional approaches for encoding DNA sequences in order to compare them with existing techniques and determine if they can provide improvements or if current methods offer superior results. Data from the 16S rRNA gene, a universal marker, was used to analyze eight bacterial groups that are significant in the pulmonary environment and have clinical implications. The bacterial genes included in this analysis are Prevotella, Abiotrophia, Acidovorax, Streptococcus, Neisseria, Veillonella, Mycobacterium, and Megasphaera. These data were downloaded from the NCBI database in Genbank file format, followed by a syntactic analysis to selectively extract relevant information from each file. For data encoding, a sequence normalization process was carried out as the first step. From approximately 22,000 initial data points, a subset was generated for testing purposes. Specifically, 55 sequences from each bacterial group met the length criteria, resulting in an initial sample of approximately 440 sequences. The sequences were encoded using different methods, including one-hot encoding, k-mers, Fourier transform, and Wavelet transform. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, were trained to evaluate these encoding methods. The performance of these models was assessed using multiple metrics, including the confusion matrix, ROC curve, and F1 Score, providing a comprehensive evaluation of their classification capabilities. The results show that accuracies between encoding methods vary by up to approximately 15%, with the Fourier transform obtaining the best results for the evaluated machine learning algorithms. These findings, supported by the detailed analysis using the confusion matrix, ROC curve, and F1 Score, provide valuable insights into the effectiveness of different encoding methods and machine learning algorithms for genomic data analysis, potentially improving the accuracy and efficiency of bacterial classification and related genomic studies.

Keywords: DNA encoding, machine learning, Fourier transform, Fourier transformation

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Authors: Pegah Eshraghi, Zahra Sadat Zomorodian, Mohammad Tahsildoost

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

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Authors: Yuen Man Yan

Abstract:

Peer support has been a rising prevalent mental health service in the globe. The community-based mental health services employ persons with lived experience of mental illness to be peer support workers (PSWs) to provide peer support service to those who are in the progress of recovery (PIRs). It represents the transformation of mental health service system to a recovery-oriented and person-centered care. Literatures proved the feasibility and effectiveness of the peer support service. Researchers have attempted to explore the unique good qualities of peer support service that benefit the PIRs. Empirical researches found that the strength of the relationship between those who sought for change and the change agents positively related to the outcomes in one-to-one therapies across theoretical orientations. However, there is lack of literature on investigating the relationship building between the PSWs and PIRs in the one-to-one community-based peer support service. This study aims to identify and characterise the relationship in the community-based one-to-one peer support service from the perspectives of PSWs and PIRs; and to conceptualize the components of relationship building between PSWs and PIRs in the community-based one-to-one peer support service. The study adopted the constructivist grounded theory approach. 10 pairs of the PSWs and PIRs participated in the study. Data were collected through multiple qualitative methods, including observation of the interaction and exchange of the PSWs and PIRs in the 1ₛₜ, 3ᵣ𝒹 and 9th sessions of the community-based one-to-one peer support service; and semi-structural interview with the PSWs and PIRs separately after the 3ᵣ𝒹and 9ₜₕ session of the peer support service. This presentation is going to report the preliminary findings of the study. PSWs and PIRs identified their relationship as “life alliance”. Empathy was found to be one of key components of the relationship between the PSWs and the PIRs. Unlike the empathy, as explained by Carl Roger, in which the service provider was able to put themselves into the shoes of the service recipients as if he was the service recipients, the intensity of the empathy was much greater in the relationship between PSWs and PIRs because PSWs had the lived experience of mental illness and recovery. The dimensions of the empathy in the relationship between PSWs and PIRs was found to be multiple, not only related to the mental illness but also related to various aspects in life, like family relationship, employment, interest of life, self-esteem and etc.

Keywords: person with lived experience, peer support worker, peer support service, relationship building, therapeutic alliance, community-based mental health setting

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Authors: H. Booke, R. Nordmeier

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Sudden cardiac arrest is sometimes eye-witnessed by kids. Mostly, their (grand-)parents are affected by sudden cardiac arrest, putting these kids under enormous psychological pressure: Although they are more than desperate to help, they feel insecure and helpless and are afraid of causing harm rather than realizing their chance to help. Even years later, they may blame themselves for not having helped their beloved ones. However, the absolute majority of school children - at least in Germany - is not educated to provide first aid. Teaching young kids (5th grade) in basic life support thus may help to save lives while washing away the kids' fear from causing harm during cardio-pulmonary resuscitation. A teaching of circulatory and respiratory (patho-)physiology, followed by hands-on training of basic life support for every single child, was offered to each school in our district. The teaching was performed by anesthesiologists, and the program was called 'kids can save lives'. However, before enrollment in this program, the entire class must have had lessons in biology with a special focus on heart and circulation as well as lung and gas exchange. More than 1.000 kids were taught and trained in basic life support, giving them the knowledge and skills to provide basic life support. This may help to reduce the rate of failure to provide first aid. Therefore, educating young kids in basic life support may not only help to save lives, but it also may help to prevent any feelings of guilt because of not having helped in cases of eye-witnessed sudden cardiac arrest.

Keywords: teaching, children, basic life support, cardiac arrest, CPR

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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: V. Rashtchi, H. Bizhani, F. R. Tatari

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This paper presents a new online loss minimization for an induction motor drive. Among the many loss minimization algorithms (LMAs) for an induction motor, a particle swarm optimization (PSO) has the advantages of fast response and high accuracy. However, the performance of the PSO and other optimization algorithms depend on the accuracy of the modeling of the motor drive and losses. In the development of the loss model, there is always a trade off between accuracy and complexity. This paper presents a new online optimization to determine an optimum flux level for the efficiency optimization of the vector-controlled induction motor drive. An induction motor (IM) model in d-q coordinates is referenced to the rotor magnetizing current. This transformation results in no leakage inductance on the rotor side, thus the decomposition into d-q components in the steady-state motor model can be utilized in deriving the motor loss model. The suggested algorithm is simple for implementation.

Keywords: induction machine, loss minimization, magnetizing current, particle swarm optimization

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Authors: B. Özlük, A. Karaaslan

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This study has been conducted as a supplementary and relationship seeking study with the purpose of measuring the tendency and success of support among peers amid nursing students studying at university in solving interpersonal problems. The population of the study (N:279) is comprised of nursing students who are studying at one state and one private university in the province of Konya, while its sample is comprised of 231 nursing students who agreed to take part in the study voluntarily. As a result of this study, it has been determined that the peer support and interpersonal problem solving characteristics among students were at medium levels and that the interpersonal problem solving skills of students studying in the third year were higher than those of first and second year students. While the interpersonal problem solving characteristics of students who are aged 20 and over were found to be higher, no difference could be determined in terms of the interpersonal problem solving skills and tendencies among students, based on their gender and where they reside. A positive – to a medium degree – and significant relationship was determined between peer support and interpersonal problem solving skills, and it is possible to say that as peer support increases, so do the skills and tendencies to solve problems.

Keywords: nursing students, peer support, interpersonal problem, problem solving

Procedia PDF Downloads 267