Reviewerly/exHarmony huggingface dataset:
Modeling of students approaches to learning in virtual education based on perceived learning environment

Background and Aim: Assessing and understanding the educational status of students in the context of online education is important in the educational psychology field. Students may show different approaches to learning in different educational settings (online, in person). In this study, students' learning approaches (deep and superficial) in online education were examined based on perceived learning environment and academic emotions. Method: The current research is a descriptive-correlational type of research using structural equations. For this purpose, the Perception of Learning Environment (Roff et al., 2001) and the Two-Factor Revised Learning Approaches Questionnaire (Mokhtar et al., 2010) were distributed among 223 students from the University of Science Lorestan medicine. Structural equivalence method with AMOS software was used to analyze the research data. Results: The results showed that the dimensions of the online learning environment predict the learning approaches, positive and negative academic emotions of students (P&lt;0.001). Findings also showed that the dimensions of the learning environment have a significant effect on deep and superficial approaches due to positive and negative emotions (P&lt;0.001). Conclusion: It can be concluded that students' learning approaches in cyberspace along with accurate and specialized planning can have the desired result.
Comparison of the effect of cognitive-balance rehabilitation with Orton-Davis integrated method in the form of distance education on the overall performance of reading and understanding words and text comprehension of dyslexic children.

Background and Aim: reading is the most important acquired skill and the most basic learning tool for students, and acquiring this skill is one of the main goals of primary education in every country. This research aims to compare of the effect of cognitive-balance rehabilitation with Orton-Davis integrated method in the form of distance education on the overall performance of reading and word comprehension and text comprehension of dyslexic children. Methods: The research method in this study was semi-experimental and pre-test-post-test design with a control group. The statistical population of this study was all dyslexic male and female primary school students who attended learning disorders centers in Tehran in 2019-20. To measure the dependent variable of the study, the Nema reading and dyslexia test (Karami Nouri and Moradi, 2014) was used. In this research, two educational interventions of the integrated Orton-Davis method, which is one of the multi-sensory methods and includes tasks based on directional games, were designed and formulated by Atchershawi (2019), to improve reading skills and reduce dyslexia, and Bar-on cognitive-balance rehabilitation. Which have been used in the form of an educational package and include assignments focusing on strengthening executive functions and rescue attention (2016). Results: The results showed that cognitive-balance rehabilitation with the integrated Orton-Davis method in the form of distance education on overall reading performance (P&lt;0.001) and word comprehension (P&lt;0.001) and text comprehension (P&lt;0.001) was effective in dyslexic children. Conclusion: It can be concluded that both the cognitive-balance rehabilitation training and the integrated Orton-Davis method in the form of distance education have an effect on the overall performance of reading and understanding words and text comprehension of dyslexic children and And it can be used to reduce the problems of dyslexic children.
Validation of the effectiveness of learning in the virtual education system of Bojnord universities

Background and Aim: As a result of the changes and developments in technology, the phenomenon of education has also gone out of its traditional form and has appeared in the form of distance education or electronic education. This research aims to validate the effectiveness of learning in the virtual education system of Bojnord universities. Methods: This research was exploratory in the mixed-method research approach. The statistical population in the qualitative section is 18 faculty members and university lecturers from the universities of Khorasan provinces (Northern, Southern, and Razavi) who were selected in a purposeful and accessible manner. Statistical population of the quantitative part of the study included all academic staff members, faculty teachers, virtual education experts, and doctoral students of North Khorasan, South Khorasan, and Razavi universities were. The sample size was calculated based on Cochran's formula of 347 samples. Results: Based on the analysis of qualitative data obtained from exploratory interviews and coding and content analysis of the text of the interviews and, at the same time, their compliance with the theoretical foundations, and according to the opinion of the participants: The main categories and factors (dimensions) and sub-factors (components) influencing in increasing the effectiveness of virtual training can be classified into 5 categories or main dimensions and 40 sub-factors. The main effective dimensions are: From the administrative, administrative, and organizational factors of the university, the curriculum and educational design factors, the factors related to a professor's and comprehensive qualifications, the technological equipment and information technology infrastructure factor, and the cultural factors of virtual space. Finally, each identified component was examined through a questionnaire, and the results showed that the model has a good fit. Conclusion: Therefore, it is strongly suggested that Azad University managers take the necessary measures and arrangements for students to access virtual platforms, high-speed internet, quality equipment, and update the equipment.
Examining Aggression, Rumination and Self-Control under the Influence of Emotion Regulation Training and Mindfulness Training in Students with Low Academic Performance

Background and Aim: Academic performance has the main role in the students' future success. The purpose of this study was to examine aggression, rumination and self-control under the influence if effectiveness of emotion regulation training and mindfulness training in female students with low academic performance in Junior high school in Tabriz. Methods: The research design was semi-experimental with pre-, and post-test, two experimental groups and one control group. The statistical population included all the female junior high school students in Tabriz, region one with low academic performance among them 75 students were selected as the research sample and randomly assigned in three groups (two experimental and one control group). The instruments of data collection included: Tangey's self-control scale, Boss &amp; Perry' aggression questionnaire and Nolen-Hoeksema &amp; Marrrow' mental rumination scale. Emotion regulation package was performed in one experimental group and mindfulness package was performed in the other experimental group. Control group received no intervention. The research hypotheses were analyzed by one way ANCOVA. Results: The findings indicated that the method of emotion regulation training is more effective on aggression than mindfulness training, but regarding rumination and self-control, no significant difference was observed between two groups. Also, the results showed that there was significant difference among two experimental groups, i.e. emotion regulation training and mindfulness training and control group regarding aggression, rumination and self-control and effectiveness of these methods on decreasing mental rumination and aggression and increasing self-control was confirmed. (P=0.05). Conclusion: The results of this research showed that the use of emotion regulation and mindfulness methods can be effective in reducing aggression and mental rumination and increasing self control of students with low academic performance
In-Depth Analysis of Contemporary Grayscale Image Dehazing Methods: A Comprehensive Review

Since the fog results in loss and color distortion in an image under poor weather circumstances, an efficient image defogging algorithm is required for retrieving various details in an image and hence recovery of a true color information for visibility improvement. In this paper several state-of-art techniques designed by different researchers towards solving this problem in order to enhance the image by improving the existing present day approaches have been presented. Various enhancements in this area are suggested to increase the value of quality parameters which leads to the better defogged image and hence decreases the road accidents existing day by day. Several intelligent techniques including Q-Deep learning methods with other computer vision algorithms have been suggested to improve upon the visibility of the foggy scene. Finally, the whole review have been concluded by pointing out the several advantages and shortcomings of the existing literature.
Analisis Sentimen Pengungkapan Informasi Manajemen: Text Mining Berbasis Metode VADER

Financial statements present information related to the firm’s financial condition. However, there is various types of data are provided to help us in assessing and understanding the firm’s business condition. The most abundant firm-related data that are available are in the form of text. It can include annual reports, official websites, or even social media posts that may contain non-financial data. Nonfinancial information is also important to help the interpretation of financial information. The research aims to analyze the sentiment of management discussion and analysis included in the manufacturing company's annual report. The research sample is manufacturing companies listed on the Indonesia Stock Exchange (IDX) during the 2016-2020 period in a row. There are 102 companies or 510 research observations include in this research. This research uses a sentiment analysis technique based on a lexicon-based approach using the VADER method. Meanwhile, the sentiment analysis process will be assisted by the Orange Data Mining application. The results of the research show that positive disclosure is greater than negative sentiments. The pattern of sentiment for companies that are classified based on sector, company size (total assets and total sales), profitability (ROI), and liquidity (current ratio) shows relatively the same results. These results illustrate that company characteristics do not make a difference in the choice of word sentiment in manufacturing companies.
Evaluasi Efektivitas Penggunaan Teknologi Pembelajaran Interaktif dalam Mata Pelajaran Matematika di Sekolah Dasar

The aim of this study was to evaluate the effectiveness of using interactive learning technology to enhance students' understanding in mathematics at SDN Genteng 1 elementary school. Employing a quantitative approach and an experimental research design, two groups of students (30 students per group) were involved in this study: an experimental group receiving instruction with interactive technology, and a control group receiving conventional instruction. Data were collected through pre-tests and post-tests as well as observation of student interaction with the interactive technology during the learning period. The results indicated that students in the experimental group experienced a significant improvement in understanding compared to the control group. The average post-test scores for the experimental group showed a greater increase compared to the control group. Thus, the use of interactive learning technology proved to be effective in enhancing students' understanding in mathematics at the elementary school level. This finding underscores the importance of integrating technology in education to create engaging and effective learning environments. Therefore, it is recommended that instructional approaches integrating interactive technology continue to be developed and implemented in teaching practices. Further steps are necessary to deepen understanding of how interactive technology can be effectively integrated into education and to identify the best strategies for optimizing its benefits for student learning outcomes. This abstract provides a comprehensive overview of the research findings and their implications for educational practice.
Student Orientation Recommender System using TOPSIS and AHP

The process of traditional school guidance is carried out by specialists. They make astudy of the student files based on the marks of the first year and the second year ofthe baccalaureate. Considering the progressive number of students and also the lack oftime to make the decision, as the selection of the specialty has a great effect on theacademic course of the students, we have realized a system of specialtyrecommendation, to computerize the orientation process and save time. But the majorproblem is that the students do not care about this process and pay no attention to itdespite its importance. As well as the software which makes the orientation ischargeable.To order these specialties to take the best specialty. We have arrived at aproblem of multi-criteria which makes it impossible to make a decision with thesecriteria. Because these criteria do not have the same importance and also are notcompatible, as there are criteria that must be maximum and other criteria must beminimal. To solve this problem, two systems of orientation and academicreorientation of students have been implemented. In both systems, the SMOTEmethod has been used to balance the learning data in the preprocessing phase. Then inthe treatment phase, we sorted the specialties using in the first system, a hybridizationof TOPSIS method and the information gain to find the weights of the criteria used,and in the second system, we used a hybridization of the AHP method andinformation gain. The results obtained indicate that before balancing data using the SMOTE method, the total accuracy of TOPSIS (84.20%) is higher than the total accuracy of AHP (83.71%). After applying balancing data using the SMOTE method,the total accuracy has increased. The total accuracy of TOPSIS (91.35%) is alsohigher than the total accuracy of AHP (90.83%). For the complexity of the twomethods, it is related to the number of criteria and the number of alternatives. If thenumber of criteria is more than 10 criteria, the complexity of TOPSIS is less than thecomplexity of AHP, and vice versa. The complexity of the two methods also dependson the number of alternatives, if the number of alternatives exceeds 10, the TOPSISmethod becomes more complex than the AHP method. In general, the system basedon TOPSIS method and the information gain is more precise than the system based onthe AHP method and the information gain. But the complexity of the AHP method isless than the complexity of the TOPSIS method.
UPAYA MENINGKATKAN KEMAMPUAN MEMBACA PERMULAAN ANAK MENGGUNAKAN KARTU HURUF PADA KELOMPOK B

&#x0D; &#x0D; &#x0D; &#x0D; Abstract&#x0D; This study aims to improve children’s initial reading skills in group B at Cahaya Baru, Malanuza Satu village, Golewa district, Ngada District for the 2021/2022 academic year by using letter cards as media. The type of research is classroom action research. The implementation of this research activity uses the Kemmis and Mc. Taggart (Kusuma and Dwigtama, 2010 :21), with the following research steps: plan, act, observer and reflect.&#x0D; Subjects in the study of group B children aged 5- 6 years at TKK Cahaya Baru Malanuza Satu village, consisten of 12 children. The object of this study is language ability (start reading). Data collection methods using observation and documentation methods. Any activities carried out, is an activity related to the ability to read the beginning is to pronounce the alphabet A- Z, recognize vowels and consonants, connect letters into simple words, and imitating A- Z writing. The increase can be proven by the percentage gain which reaches 75% in cycle II. This through the media of letter cards, the initial reading ability of group B children in TKK Cahaya Baru has increased. Therfore teachers are advised to prepare more appropriate learning media to attract students’ interesy in carrying out learning activities.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
USE OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF SOLAR ENERGY PRODUCTION FROM PHOTOELECTRIC ELEMENTS

Purpose. Development of an artificial neural network for modeling time series of forecasting electricity generation taking into account the use of a renewable energy source using MATLAB&amp;Simulink software. Methodology. Theoretical and experimental research with the help of software implementation of fundamental knowledge in the field of electricity generation using renewable energy sources; simulation of artificial neural network prediction; methodology of mathematical statistics and forecast analysis. Results. Based on the conducted research and implemented solutions, this article developed a time series modeling system for forecasting solar energy production using Simulink. Defined control parameters and corresponding time series; the analysis of the input data showed the influence on the generated electrical energy of such meteorological parameters as solar radiation on the surface of the earth and on the surface of the panel, temperature of the panel and the environment, cloud cover and time of day. In order to ensure the appropriate accuracy of forecasting the generated power, there was a task to ensure the accuracy limit of forecasting meteorological parameters. Scientific novelty. The novelty of the given research lies in the formation of the balance of electricity in the power system with the help of an artificial neural network, influencing the choice of mode parameters and calculated loads. The balance between production and consumption of electricity is a fundamental aspect of the technological sustainability of the energy system. Violation of this balance can lead to deterioration of power quality, including changes in frequency and voltage in electrical networks, which can affect the efficiency of equipment. Practical significance. Neural network forecasting allows effectively distribute the load between different objects of the power system. This makes it possible to manage electricity consumption by adjusting the load of equipment. For example, the main electricity generation can be shifted to hours and zones of the wholesale electricity market with lower prices to reduce the cost of electricity generated from renewable energy sources.
GAP ANALYSIS OF THE EXISTING HOSPITAL MANAGEMENT INFORMATION SYSTEM AND DESIGNING REQUIREMENTS FOR THE NEW INFORMATION MANAGEMENT SYSTEM

KETAN APTE, Dr. P.R. SODANI
BLOCKCHAIN TECHNOLOGY: OPPORTUNITIES IN HEALTHCARE

FARHEENZEBA SIDDIQUE, Dr. P.R. SODANI
SOM-XG: Self-Organizing Map Based Resampling with Sample Extraction and Generation

The “Data Imbalance Problem” is a well-defined and challenging problem in the Machine Learning domain addressed throughout the past decades. With the emergence of Big Data, addressing the data imbalance has reemerged as a trending topic because traditional solutions for this problem are inadequate with the increasing volume and dimensionality of data. There exist a wide range of solutions, from data-level to algorithmiclevel, proposed to address the data imbalance problem. Among these approaches, data-level approaches are popular among the scientific community because of their inherent classifier independence, making them generalizable over many different domains. Oversampling is one such data-level technique frequently explored by researchers, especially in extreme imbalance scenarios. This study introduces SOM-XG, an oversampling technique capable of addressing even the extreme imbalance scenarios. The proposed technique utilizes two Self-Organizing Maps and exploits their properties to address the within and between class imbalances and the decision boundary preservation, generating new synthetic samples that are topologically similar to the original samples in the dataset. The empirical results obtained for datasets with imbalance ratios ranging from 1.38 to 130, the number of features ranging from 3 to 300, and the number of samples ranging from 150 to 145,751, oversampled using SOM-XG, demonstrate enhanced classification results while consistently outperforming other state-of-the-art techniques.
Digitize the eCommerce Bookkeeping – An Automation Bookkeeping Prototype Development

Abstract - This study explores the impact of digital transformation on e-commerce, specifically focusing on the automation of bookkeeping processes. Despite the widespread benefits of e-commerce growth, a significant management gap persists, particularly in smart transaction management for retailers. The absence of automated systems results in manual recording methods, posing risks of data corruption and unreliability. Current practices involve e-commerce retailers relying on conventional ledgers or manual bookkeeping systems. To address these challenges and leverage digital transformation, retailers are turning to smart ledgers and price prediction systems. Employing a mixed-methods approach, the study combines quantitative insights from a Facebook poll with qualitative data extracted from discussions. The poll reveals prevalent manual bookkeeping practices among 49 e-commerce retailers, emphasizing the need for automation. Comparative analysis of existing applications identifies gaps, particularly the absence of automatic pricing suggestions crucial for-profit generation based on e-commerce programs. The research culminates in the development of a mobile application prototype, Manledger, utilizing the Rapid Application Development (RAD) methodology. Manledger aims to streamline e-commerce bookkeeping by automating sales and inventory tracking, profit calculations, and pricing strategies based on seller programs. Results demonstrate the prototype's effectiveness in enhancing efficiency and accuracy, supported by positive user feedback. Acknowledging the need for further exploration, the study suggests future research directions. These include long-term impact assessments of automated systems, comparative studies across diverse platforms, integration of advanced technologies like AI and blockchain, cybersecurity considerations, and investigations into user experience and adoption challenges. This comprehensive approach seeks to advance understanding and contribute to innovative solutions in the dynamic realm of e-commerce financial management.