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29,939 Article Results

Dengue hemorrhagic fever vulnerability assessment in Gorontalo Regency using analytic hierarchy process and geoinformation techniques

10.11591/ijphs.v11i1.21084
Ririn Pakaya , Yanti Hz. Hano , Muhammad Ramdhan Olii
One method of reducing the spread of dengue hemorrhagic fever (DHF) is to provide a map of DHF-prone locations based on spatial analysis. The major way to prevent the spread of DHF is to manage and control its vector by focussing on specific regions of localisation and removing appropriate breeding circumstances. Spatial analysis can detect DHF clusters that are larger than expected based on the underlying data. This research aimed to identify and map DHF vulnerability zones based on many parameters within the scope of the analytical hierarchy and geographical information systems (GIS). We found that the consistency ratio of 0.079 for analytic hierarchy process (AHP) factor weights was judged to be satisfactory. The population density, distance to the road, and distance to health facilities were shown to be the most relevant factors in determining DHF vulnerability. Gorontalo Regency is dominated by low vulnerability classes with an area of 139,493.5 ha or 65.08% of the total area. The GIS-AHP process could be used to assess transmissible DHF vulnerability zonation, which would aid in improving surveillance strategies for DHF and other vector-borne diseases in order to encourage prevention and control actions.
Volume: 11
Issue: 1
Page: 138-148
Publish at: 2022-03-01

Determinants of health-related quality of life in Iranian patients after recovery from COVID-19: demographic influences and insomnia

10.11591/ijphs.v11i1.21039
Mohsen Saffari , Hormoz Sanaeinasab , Hojat Rashidi-Jahan , Amir Pakpour Hajiagha , Hosein Mahmoudi , Faten Al-Zaben , Harold George Koenig
The current study sought to identify factors that may affect health-related quality of life (HRQoL) in patients recovering from COVID-19 infection in Iran. In a cross-sectional study 258 patients diagnosed with COVID-19, participants completed a questionnaire approximately one month after hospital discharge when demographic and clinical factors (including insomnia) and HRQoL were assessed. A logistic regression was used. Age, gender, marital status, education, having child, early physician visit, early diagnosis, early hospitalization, symptom type, Rhesus factor, and level of insomnia were associated with various components of HRQoL (p<0.05). In multivariate analyses, poorer physical HRQoL was independently associated with female gender (OR=4.53; 95% CI=2.22-2.29), initial symptom of cough (OR=2.73; 95% CI=1.26-5.94), and insomnia (OR=2.74; 95% CI=1.22-6.14). Poorer mental HRQoL was associated with being age 40 years or older (OR=1.90; 95% CI=1.02-3.54), female gender (OR=2.48; 95% CI=1.26-4.88), initial symptom being cough (OR=3.12; 95% CI=1.46-6.68), and insomnia (sub-threshold insomnia, OR=3.19; 95% CI, 1.51-6.74, to severe insomnia, OR=3.86; 95% CI=1.35-11.07). Healthcare professionals should be aware that older people, female gender, those with initial symptom of cough, and insomnia may be at greater risk for poor quality of life following hospital discharge.
Volume: 11
Issue: 1
Page: 220-231
Publish at: 2022-03-01

Undiscovered voices: Motivation and demotivation factors in learning English among Indonesian orphan students

10.11591/ijere.v11i1.21262
Zidni Ma’ruf , Bambang Widi Pratolo , Okta Widia Sari , Arlischa Ardinengtyas
This qualitative descriptive research aimed to investigate factors of motivation and demotivation in learning English among Indonesian orphan students. There were eight participants who live and study in an Indonesian orphanage. They were randomly chosen in this research. The data were mainly collected through individual semi-structured interviews and focus group discussion (FGD) sections. All interviews and FGD sections data were recorded, transcribed, analyzed, and divided into several themes and subthemes. Further, member checking was done to develop the trustworthiness of the research. The research revealed that internal and external factors were positively related to motivation and demotivation in learning English among Indonesian orphan students. Further, suggestions and policy recommendations were all offered to related parties to support and give equal educational facilities to all students since they have the same right for education.
Volume: 11
Issue: 1
Page: 441-448
Publish at: 2022-03-01

Text similarity algorithms to determine Indian penal code sections for offence report

10.11591/ijai.v11.i1.pp34-40
Ambrish Srivastav , Shaligram Prajapat
Taking decisions by comparing two text documents is a new innovative idea. Text documents contain details, rules and information related to a domain. The judiciary system is an area where many textual documents are available. In some documents, rules related to the judiciary are mentioned, such as the Indian penal code (IPC) section documents and other documents like first information report (FIR), and Investigation report. contain details of incidents. Our assumption is that the system can help in making the decision by finding the right IPC Section from the result of text similarity between IPC section document and FIR, investigation report. In this research paper, we preface a new research problem to make decisions to suggest appropriate IPC Section for crime related information from user’s input by using vector space model and natural language processing techniques.
Volume: 11
Issue: 1
Page: 34-40
Publish at: 2022-03-01

Fingerprint recognition based on collected images using deep learning technology

10.11591/ijai.v11.i1.pp81-88
Ali Fadhil Yaseen Althabhawee , Bashra Kadhim Oleiwi Chabor Alwawi
The fingerprint identification is the most widely used authentication system. The fingerprint uniqueness for each human being provides error-free identification. However, during the scanning process of the fingerprint, the generated image using the fingerprint scanner may differ slightly during each scan. This paper proposes an efficient matching model for fingerprint authentication using deep learning based deep convolutional neural network (CNN or ConvNet). The proposed deep CNN consists of fifteen layers and is classified into two stages. The first stage is preparation stage which includes the fingerprint images collection, augmentation and pre-processing steps, while the second stage is the features extraction and matching stage. Regarding the implantation results, the proposed system provided the best matching for the given fingerprint features. The obtained training accuracy of the proposed model is 100% for training dataset and 100% for validating dataset.
Volume: 11
Issue: 1
Page: 81-88
Publish at: 2022-03-01

Oral participation practices in classroom among university students in Afghanistan

10.11591/ijere.v11i1.21865
Hamza Atifnigar , Hedayatullah Bawar , Malang Momand , Siti Aishah Abdul Hamid
This study aimed at exploring factors affecting classroom participation among students in the English department of Laghman University, Afghanistan. More precisely, this research discovered factors related to teachers and class-size that hinder students’ practice of oral participation in the classroom. In collecting the data, this study employed mixed-method research with concurrent design. An adapted questionnaire and a semi-structured interview have been used as the data collection instrument of this study. An online survey questionnaire was conducted with 110 respondents. In addition, a semi-structured interview was conducted with five of the respondents at the English Department of Laghman University. The data from the questionnaire was descriptively analyzed through using statistical package for social science (SPSS), and the semi-structured interview data were thematically analyzed and interpreted. The findings revealed that class-size related factor is the first influential factor in affecting oral participation among students and it is due to having large number students in a class. Teacher’s related factor is the second influential factor that affects the oral participation of students. Majority claimed that teachers’ approach, behavior, and qualification have prominent impact on their level of oral participation. Based on the finding, it can be seen that class-size related, and teachers’ factors affected oral participation of the students at the English Department of Laghman University. It is recommended that the Ministry of Higher Education of Afghanistan strive to decrease the number of students in each class and enforce teachers in implementing a student-centered learning approach while teaching.
Volume: 11
Issue: 1
Page: 409-420
Publish at: 2022-03-01

AraBERT transformer model for Arabic comments and reviews analysis

10.11591/ijai.v11.i1.pp379-387
Hicham EL Moubtahij , Hajar Abdelali , El Bachir Tazi
Arabic language is rich and complex in terms of word morphology compared to other Latin languages. Recently, natural language processing (NLP) field emerges with many researches targeting Arabic language understanding (ALU). In this context, this work presents our developed approach based on the Arabic bidirectional encoder representations from transformers (AraBERT) model where the main required steps are presented in detail. We started by the input text pre-processing, which is, then, segmented using the Farasa segmentation technique. In the next step, the AraBERT model is implemented with the pertinent parameters. The performance of our approach has been evaluated using the ARev dataset which contains more than 40,000 comments-remarks records relate to the tourism sector such as hotel reviews, restaurant reviews and others. Moreover, the obtained results are deeply compared with other relevant states of the art methods, and it shows the competitiveness of our approach that gives important results that can serve as a guide for further improvements in this field.
Volume: 11
Issue: 1
Page: 379-387
Publish at: 2022-03-01

Text mining approaches for analyzing an Indonesian tafseer and translation of the holy Quran

10.11591/ijeecs.v25.i3.pp1469-1480
Media Anugerah Ayu , Edi Irawan , Teddy Mantoro
The Indonesian tafseer and translation of Holy Quran is an important source of information and knowledge for Indonesian muslims, since not many Indonesian muslims understand Arabic language in the Quran.  However, the tafseer is full of the commentaries and explanation of each surah (chapter) and/or ayah (verse), which form a large document and not so easy to be accessed. Thus, the challenge is how to refer to both tafseer and translation in faster and accurate ways as one needs to always refer to them back and forth. Hence, this study proposes several text mining approaches, i.e.  most frequent words, K-means clustering, and association rules, to analyze an Indonesian tafseer and translation of Quran and provide insights of hidden knowledge and relationships based on statistical information derived from it.   These insights could be useful for muslims in general and for people that doing research in related areas.  This study shows interesting results from combined analysis of the approaches used which can help people accessing information in tafseer more efficiently.  As well, interesting relationships have been drawn from terms in the tafseer which could provide further and deeper knowledge on messages in the Quran.
Volume: 25
Issue: 3
Page: 1469-1480
Publish at: 2022-03-01

Machine learning algorithms for electrical appliances monitoring system using open-source systems

10.11591/ijai.v11.i1.pp300-309
Viet Hoang Duong , Nam Hoang Nguyen
Two main methods to minimize the impact of electricity generation on the environment are to exploit clean fuel resources and use electricity more effectively. In this paper, we aim to change the user's electricity usage by providing feedback about the electrical energy consumed by each device. The authors introduced two devices, load monitoring device (LMD) and activity monitoring device (AMD). The function of the LMD is to provide feedback on the operating status and energy consumption of electrical appliances in a home, which will help people consume electrical energy more efficiently. The parameters of LMD are used to predict the on/off state of each electrical appliance thanks to machine learning algorithms. AMD with audio sensors can assist LMD to distinguish electrical devices with the same or varying power over time. The system was tested for three weeks and achieved a state prediction accuracy of 93.60%.
Volume: 11
Issue: 1
Page: 300-309
Publish at: 2022-03-01

Privacy preserving human activity recognition framework using an optimized prediction algorithm

10.11591/ijai.v11.i1.pp254-264
Kambala Vijaya Kumar , Jonnadula Harikiran
Human activity recognition, in computer vision research, is the area of growing interest as it has plethora of real-world applications. Inferring actions from one or more persons captured through a live video has its immense utility in the contemporary era. Same time, protecting privacy of humans is to be given paramount importance. Many researchers contributed towards this end leading to privacy preserving action recognition systems. However, having an optimized model that can withstand any adversary models that strives to disclose privacy information. To address this problem, we proposed an algorithm known optimized prediction algorithm for privacy preserving activity recognition (OPA-PPAR) based on deep neural networks. It anonymizes video content to have adaptive privacy model that defeats attacks from adversaries. The privacy model enhances the privacy of humans while permitting highly accurate approach towards action recognition. The algorithm is implemented to realize privacy preserving human activity recognition framework (PPHARF). The visual recognition of human actions is made using an underlying adversarial learning process where the anonymization is optimized to have an adaptive privacy model. A dataset named human metabolome database (HMDB51) is used for empirical study. Our experiments with using Python data science platform reveal that the OPA-PPAR outperforms existing methods.
Volume: 11
Issue: 1
Page: 254-264
Publish at: 2022-03-01

Group investigation model to improve interpersonal skills

10.11591/ijere.v11i1.21914
Nur Ainiyah , Anik Ghufron , Marzuki Marzuki , Said Subhan Posangi , Kasim Yahiji , Abdul Rohman , Moch. Tolchah , St. Wardah Hanafie Das
This study aimed to prove the effectiveness of the application of the group investigation learning model in improving students' interpersonal skills. The sample of this study was 116 students, which was determined by a simple random sampling technique. This experimental research used pre-test post-test Control Group Design. Data were obtained by direct observation of the interpersonal skills of students during the learning process. Final observation score of interpersonal skills is 0.026 and the t value count greater than t table (2.272>1.980). Thus, there are differences in interpersonal skills between the experimental class and the control class. This means that the use of the group investigation model is effective in improving students' interpersonal skills.
Volume: 11
Issue: 1
Page: 467-474
Publish at: 2022-03-01

Effect of socio-economic status on the prevalence of diabetes mellitus in Indonesia

10.11591/ijphs.v11i1.21080
Amalia Kusumaningrum , Rico Ricardo
The case of diabetes mellitus in Indonesia shows an increasing trend, especially in the upper socio-economic groups. This study aimed to determine the effect of socioeconomic status on the prevalence of diabetes mellitus in Indonesia using data from the Indonesia family life survey (IFLS). This study was intentionally targeted at individuals aged 15 years and over who are respondents to IFLS-5. Diabetes mellitus becomes the dependent variable, while the independent variables comprise age, gender, education, income, body mass index, and smoking behavior. Logistic regression was performed to determine the potential socio-economic factors associated with diabetes mellitus. The results reveal that socio-economic status influences the prevalence of diabetes mellitus in Indonesia. The higher the income level, the probability of diabetes mellitus increases by 0.466 percent. Meanwhile, individuals who attended university had a 2.86 percent higher probability than individuals with a primary level of education.
Volume: 11
Issue: 1
Page: 281-286
Publish at: 2022-03-01

Organizational safety climate and workplace violence among primary healthcare workers in Malaysia

10.11591/ijphs.v11i1.20929
Sudeash Rajakrishnan , Victor Hoe Chee Wai Abdullah , Nasrin Aghamohammadi
Workplace violence (WPV) has become a global safety and health concern in recent times particularly in the healthcare sector. In addition, low levels of organisational safety climate (OSC) have been associated with higher occurrence of occupational related health outcomes. Hence, the objective of this study was to determine the association between organisational safety climate and workplace violence among government primary healthcare workers. A cross-sectional study among a stratified random sample of 838 primary healthcare workers (HCW) from the nine district health offices under the Selangor state health department. Two standardized self-administered questionnaires were used to obtain data on WPV and OSC. Logistic regression models used to estimate the association between OSC and WPV. Prevalence of WPV was 68.5% whereby verbal abuse was the most common type (65%) followed by bullying (27%), physical violence (6%) and sexual harassment (2%). Nurses (29.7%) were the most affected by WPV. The main perpetrators were relatives of patients (38%). Low level of OSC was also associated with WPV (OR=3.04, 95% CI=1.45-6.41). The results of this study confirmed that safety climate is associated with WPV. Hence, interventions and efforts to prevent WPV among HCW should also include improving organizational safety factors.
Volume: 11
Issue: 1
Page: 88-97
Publish at: 2022-03-01

A review on efficiency improvement methods in organic Rankine cycle system: an exergy approach

10.11591/ijaas.v11.i1.pp1-10
Gollangi Raju , Nagamalleswara Rao Kanidarapu
Exergy, one of the handed-down energy conservation techniques, which can obtain from thermodynamic laws (first and second), will disclose the work presented within the system, the amount of irreversibility as well as what are the possible ways to reduce inefficiencies in the system. This discourse mainly highlighted various techniques and possible methods for efficiency improvement in the organic Rankine cycle (ORC). That means mainly concentrated on following key parameters like the selection of working fluid, suitable expander, the different heat sources of an evaporator, and modifications in heat exchanger based on the application of ORC system through an exergy approach for better performance, decrease energy losses, and destruction rate. This review can help to pontificate for better-summarized results that were done before and suggest some ideas for how to select an optimized parameter for better efficiency and to decrease the destruction rate in the ORC system. 
Volume: 11
Issue: 1
Page: 1-10
Publish at: 2022-03-01

An accurate target tracking method in wireless sensor networks

10.11591/ijeecs.v25.i3.pp1589-1598
Hanen Ahmadi , Ridha Bouallegue
Localization is a challenging research issue in various sectors of activity, particularly in dynamic indoor environment with high perturbation. Many existing localization techniques in wireless sensor networks are not efficient because of many constraints such as the high cost, the memory and energy limitation and the environmental noise effects. Thus, the development of novel methods of localization has become a great concern for the wireless sensor networks. In this paper, we propose a tracking method that combines regression tree and Kalman smoother filtering. Previously, regression tree has been suggested for static positioning by means of received signal strength indicator measurements. In this work, we employ this strategy to solve the mapping relation between these measurements and the target position by means of an optimized propagation model. Moreover, the predicted position considered as the observed information is introduced to the Kalman smoother algorithm, to have more precise state of the moving target. The proposed algorithm has been assessed and compared to other existing methods using real measurements of the received power by the moving target in an indoor environment. The evaluation shows that our solution outperforms other methods regarding localization accuracy.
Volume: 25
Issue: 3
Page: 1589-1598
Publish at: 2022-03-01
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