bluuebunny/crossref_metadata_2025_split huggingface dataset:
Customer Centric in Hospitality Industry: a Systematic Literature Review and Future Research Direction
Authors: Rini Gilang Puspita , Ferdinand Augusty Tae 
Published: January 2024

The purpose of this research is to review the literature published from 2010 to 2020 regarding customer-centric strategy in the context of the hospitality industry. The customer-centric paradigm focuses on creating value for its customers, so it becomes interesting to study in the context of hospitality. This is a literature review research with the Publish or Perish software used to obtain initial data from a total of 235 articles published from 2010-2020 according to the Scopus database. The articles were further analyzed according to the criteria, with only 87 articles. The results showed that a customer-centric strategy is a company strategy that pays attention to consumer wants and needs. The customer-centric approach is similar to the outside-in approach because they pay attention to customers and adjust their complaints to company policies and strategies. This research provides benefits for future studies on customer-centric strategy in the hospitality industry that uses big data analytics to create value for customers. Furthermore, it provides an overview of customer-centric strategy trends in terms of understanding, theoretical perspective, and setting needed for future studies.
Impact of different ENSO positions and Indian Ocean Dipole events on Indonesian rainfall
Authors: Zaini Ahmad Zul Amal , Vonnisa Mutya , Marzuki Marzuki 
Published: January 2024

The El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) are widely recognized as the leading modes of climate variability in the tropics. This paper investigates the impact of different ENSO positions and IOD events on Indonesian rainfall during the period 1950–2021. The ENSO position is determined by the largest value of four Niño indices: Niño 1+2, Niño 3, Niño 3.4, and Niño 4. These ENSO positions are hereafter referred to as El-Niño/La-Niña 1+2, El-Niño/La-Niña 3, El-Niño/La-Niña 3.4, and El-Niño/La-Niña 4, respectively. The Dipole Mode Index (DMI) was used to observe IOD events. Different ENSO positions and IOD events result in different responses to Indonesian rainfall, obtained from the European Center for Medium-Range Weather Forecasts (ECMWF) ERA-5 data. The most significant decrease in rainfall occurs during the June-to-Septempber (JJAS) season of El-Niño 3. Conversely, during El-Niño 3.4, rainfall increases in the Sumatra and part of Kalimantan regions. The most significant increase in rainfall occurs during La-Niña 3.4, followed by La-Niña 4, La-Niña 3, and La-Niña 1+2. During a positive IOD phase, the southern part of western Indonesia experiences a decrease in precipitation of more than 30%. A more significant decrease in rainfall (>40%) occurs when a positive IOD co-occurs with El-Niño. During a negative IOD phase, Indonesia's rainfall patterns become more spatially variable. An increase in rainfall is more pronounced when a negative IOD co-occurs with La-Niña. The difference in Indonesian rainfall during different ENSO positions and IOD phases is related to differences in atmosphere-ocean interaction during each condition.
Feeding and trophic interactions of plankton Crustaceans in the pelagic zone of the Azabachye Lake
Authors: Bazarkina L. A. 
Published: January 2024

Characteristics of feeding mechanisms, forage composition and methods of analysis of the diet of plankton crustacean are demonstrated based on reviewed and author’s data. Preferable forage species for the mass plankton crustaceans Cyclops scutifer and Daphnia galeata in the lake Azabachye were figured out using methods of population ecology. It is revealed, that reproduction of the cyclops and daphnia populations prospers due to Aulacoseira subarctica. In the periods of poor vegetation of the diatom the cyclops have been feeding on shell-less rotifers, and in particular years – on the diatoms of the genera Stephanodiscus. In the years of vigorous bloom of Bacillariophyta the daphnias filter small cells of diatom (S. minutulus) or blue-green (Gloeocapsa sp.) algae. In case of suggested similarity in the forage organisms between small numbered Eurytemora kurenkovi and abundant C. scutifer, there was no competition observed between the copepods. The main forage component for predator Leptodora kindti was juvenile D. galeata. It is concluded based on the analysis, that intense competition between plankton crustaceans does not raise in the Azabachye Lake.
A Typical Review of Current and Prospective Microwave and Optical Remote Sensing Datasets for Soil Moisture Retrieval
Authors: Prabhavathy Settu Et al. 
Published: January 2024

Soil Moisture content is a vital indicator of both the weather and the water cycle. It has been a long-standing difficulty for the field of remote sensing to make sense of soil moisture's spatial and temporal distribution. For over five decades, researchers across the world have exclusively investigated the optical and microwave datasets for estimating soil moisture by developing various models, and algorithms. Nevertheless, challenges are faced in the consistent retrieval of SM at local, and global scales with higher accuracy in space and time resolution. The review was conducted in-depth, looking at the methods using optical and microwave data to determine soil moisture, and outlining the benefits and drawbacks considering the current needs. With this research, a new age of widespread use of space technology for remote sensing of soil moisture has been ushered in. The study also acknowledges the scientific challenges of utilizing remote sensing datasets for soil moisture measurement.
Machine Learning based IoT Flood Rediction Using Data Modeling and Decision Support System
Authors: Deivendran P Et al. 
Published: January 2024

An essential step in supplying data for climate impact studies and evaluations of hydrological processes is rainfall prediction. However, rainfall events are complex phenomenon’s that continue to be difficult to forecast. In this paper , we present unique hybrid models for the prediction of monthly precipitation that include Seasonal Artificial Neural Networks and Discrete wavelet transforms are two pre-processing methods, together with Artificial Neural Networks have two feed forward neural networks. The temporal series of observed monthly rainfall from Vietnam’s Ca Mau hydrological station were decomposed into three subsets by seasonal decomposition and five sub signals and four levels by wavelet analysis. The methods for predicting rainfall that use feed forward artificial neural networks (ANN) and seasonal artificial neural network (SANN) were fed with the processed data. The classic genetic method and simulated annealing method backed by using an integrated moving average and autoregressive moving was contrasted with the predicted models for model evaluation. The results showed that non-stationary regarding issues with non-linear time series, such forecasting rainfall could be satisfactorily simulated. The SANN model was integrated with the wavelet transform and seasonal decomposition are both used. Techniques, however the wavelet transform method produced the most accurate monthly rainfall data, Predictions. Due to the effects of climate change, nations including the Japan, China, the United States of America, and Taiwan, etc., have recently experienced severe and devastating natural disasters. One of the biggest causes of the destruction in Asian nations like china, India, Bangladesh, Sri Lanka, etc. is the flood. The danger of fatality from these floods is increased by 78% as information technology advances; there is a demand for simple access to massive amounts of cloud storage and computing capacity.
Online Freelancing Intention among Female Graduates
Authors: Manjula Gunarathna Et al. 
Published: January 2024

Gender-based unemployment disparities persist in Sri Lanka, even though women possess high levels of education. This study examines the intentions of female university graduates to engage in online freelancing, which provides flexible work opportunities and could potentially address this issue. The study presents a research methodology that is built upon the Technology Acceptance methodology. It examines characteristics such as perceived use, ease of use, perceived security, internet literacy, and degree type in relation to the intention to engage in online freelancing. Findings reveal that perceived usefulness, ease of use, perceived security, internet literacy, and the type of degree significantly influence the intention to engage in online freelancing. The direct influence of perceived usefulness and ease of use on intention shows the need of addressing the benefits and user-friendliness of online freelancing platforms. Perceived security plays a crucial role in building trust, particularly in transactions and data sharing. Respondents with moderate internet literacy and business-oriented degrees show higher positive intentions. The study concludes by providing recommendations for online freelancing platforms to enhance perceived usefulness, user-friendliness, and security and to cater to a diverse audience, including those with varying internet literacy and educational backgrounds. Collaboration with educational institutions is suggested to bridge the gap between education and online freelancing readiness. Regular assessment of user perceptions and continuous improvement are advised to adapt to evolving user needs and preferences. This research contributes to the understanding of the factors influencing the intention of female graduates to participate in the online freelancing market in Sri Lanka.
Generative Artificial Intelligence and GPT using Deep Learning: A Comprehensive Vision, Applications Trends and Challenges
Authors: Ruchi Sharma Et al. 
Published: January 2024

Generative Artificial intelligence is a prominent and recently emerging subdomain in the field of artificial intelligence. It deals with question-answering based on natural language processing. This paper discusses recent methodologies adopted by researchers in this field. It also discusses GAI and machine learning techniques for multimodal applications like image, text and audio-based data generation. This meta-analysis and survey was done from prominent research up to 2023 from the Scopus Database consisting of reputed and authenticated research papers.The research contribution is twofold 1. To analyze the recent research and applications at the industry level 2. To identify techniques and associated limitations. This would further aid practitioners to address future challenges.
An Analytical Data Model to Improve Benefits of the Comprehensive Health Insurance System By Data Mining Techniques
Authors: Ahmed Mohamed Abd El-Badie Seif Et al. 
Published: January 2024

Health insurance represents a crucial safeguard against the myriad risks associated with individual health conditions, covering expenses related to examinations, diagnosis and treatment, as well as providing psychological and physical support. Until mid-2019, Egypt implemented a social health insurance system, and then moved to a renewed framework known as the comprehensive health insurance system in the latter half of 2019. The research at hand focuses on exploring a new mechanism through develop a new model, it can be help the General Authority for Health Insurance to attainment of maximum benefits, whether in terms of individuals medical services or the enhancement of the nation's overall income return.
The researcher depends on using the data mining technique to conduct specialized analyzes of big data for the new model, to offer the numerous advantages, including empowering decision-makers to make accurate decisions based on reliable information and proficient data analysis, Based on the new model for the health insurance system that was prepared and implemented using the Weka program, version 3.8.6, through the classifier scheme (classifiers.trees.J48 algorithm), the applied pattern was used:
(Scheme:weka.classifiers.misc.InputMappedClassifier -I -trim -Wweka.classifiers.trees.J48 -- -C 0.25 -M 2), Through the Weka program, a new model was designed that contains two parts. The first part is to classify the insured into insurance categories according to their monthly salaries, and the second part is to predict the classification and distribution of employees among insurance packages according to the new health insurance model. The model was prepared using Weka.classifiers.trees.J48. software to analysis the dataset of 2781 employees.
One of the significant outcomes that the researcher emphasizes from implementing the new model is the attainment of maximum benefits, whether in terms of individual medical services or the enhancement of the nation's overall income return. Under the existing health insurance law in Egypt, the monthly subscription fee is a flat 1% for all employees, without accounting for other factors. The results of applying the new model showed the effectiveness of the model through a comparison between the current subscription fees and the new health insurance model, developed using the same dataset of 2781 employees, reveals that the total monthly subscription fees under the current health insurance system amount to 153057.97 Egyptian Pounds (L.E.), whereas the total subscription fees under the new health insurance system model reach 365998.91 L.E., The financial benefits realized from the new model amount to 212940.94 L.E., representing a percentage increase of up to 139.12%. This demonstrates a considerable improvement in financial outcomes, and the potential advantages of transitioning to the new health insurance model.
Context-Preserving Sentiment Classification Using Bi-TCN And BI-GRU with Multi-Head Self-Attention
Authors: K. R. Srinath Et al. 
Published: January 2024

In natural language processing, sentiment classification is the recently used topic. Specifically, the objective of the sentiment analysis is to categorise the polarity expressed on the sentence's target. However, there are some researches for classifying the polarity of the target which outperforms well in their way. Yet, there are some limitations, such as apparent and in-apparent issues, gradient problems, etc., to overcome these issues the context-preserving sentiment classification using BI-TCN (Bidirectional Temporal Convolutional network) and BI-GRU (Bidirectional Gated Recurrent Unit) with Multi-head self-attention is proposed to extracts both the local dependent and global dependent information from the sentence, then it will incrementally extract the supervision information of the target to train the model. Formerly, the model is tested and trained using four datasets and the performance is compared with four existing methods, its accuracy is evaluated using the F1-score, precision, recall, specificity, and MCC (Matthews Correlation Coefficient). Consequently, the proposed approach provides the best accuracy level of 98%..
Design and Performance Analysis of a Neural Network Sliding Mode based Hybrid Controlled Islanded Micro-Grid
Authors: A. Venkat Rao Et al. 
Published: January 2024

The micro-grid fed shunt voltage source converter, also known as a smart Artificial neural network and sliding mode controller (ANN-SMC) based hybrid system, is developed during this research for solar system, wind and battery storage connected shunt filter. Here, phased locked loop utilization is avoided by the self-tuning filter (STF). Move over, apart from producing phase synchronization, STF also distinguishes between the fundamental and harmonic components. Total harmonic distortion (THD) reduction and steady voltage across dc link during load variations with short settling times under both grid and island settings are the key goals of the recommended approach. Four scenarios with various combinations of loads, solar radiation, and wind speeds under islanding conditions are used to illustrate the effectiveness of the study.
Fuzzy Controlled Power Management and Power Quality Enhancement for Grid Integrated Renewable Energy sources with EV Fed UPQC
Authors: Alvala Mahesh Kumar Et al. 
Published: January 2024

Recently, the merging of Renewable Energy Sources (RES) and Electric Vehicles (EVs) has brought about significant changes in distribution systems. The main goal of this research is to effectively tackle power quality (PQ) concerns such as voltage fluctuations(sags and swells) and total harmonic distortion(THD) in a system connected to the grid .This system combines Solar Photovoltaic(SPV) and Wind Energy Systems(WES) and integrates a Battery Energy Storage System(BESS) and Electrical vehicles(EVs). To accomplish this, a versatile Unified Power Quality Conditioner (UPQC), referred to as U-SWEBEV, is employed. The control of power flow, encompassing the transfer from energy generation sources to consumer loads and among various sources, is efficiently managed through the application of a Fuzzy Interface System (FIS) regulation. This FIS system plays a key role in regulating power distribution. Additionally, a fuzzy Controller is implemented to enhance Maximum power Point Tracking (MPPT) from the SPV system. This combined approach , utilizing both Fuzzy Logic and the Unified power Quality Conditioner (UPQC), significantly contributes to the efficient and effective. utilization of power resources while successfully addressing power quality (PQ) concerns with different load combinations . The proposed method reduces the total harmonic distortion of load current and source voltage at linear nonlinear, nonlinear, Nonlinear and unbalanced linear loads with % THD 1.64 ,0.04,1.20,0.04 and 2.84,2.32 which are lower than the existing methods that are available in literature.
Impact of Covid-19 Pandemic on User Search Behavior: A Case Study of Postgraduate Students
Authors: Obada Alhabashneh Et al. 
Published: January 2024

Relevance Feedback (RF) is crucial for building a user profile which is a fundamental element of different intelligent systems such as information retrieval, information filtering, and personalization. RF is affected by a number of contextual factors such as mood, stress level, and sentimental state of the user. Covid-19 pandemic imposed dramatic changes to the user environment as well as the search context. This paper investigates user’s search behaviour to identify the differences in the behavior between the contexts before and during the Covid-19 pandemic. This can be practically translated into identifying the differences in the relationship between the implicit feedback and the explicit relevance level between the two contexts. For this purpose, we conducted three user studies (i) Pre-COVID-19, (ii) Mid COVID-19 and (iii) after Covid-19. A user study was conducted on the same group of users on the three user studies. The Pre-COVID-19 user study took place before the pandemic started and the Mid-COVID-19 user study took place three months after the beginning of the pandemic. After Covid-19 stage took place after 18 months of the pandemic. A linear regression model was developed for each user study using IBM-SPSS. The analysis showed a significant variation in the user behavior between the two studies due to the COVID-19 context and its impact on user search behaviour. Also, two new RF parameters in Mid-COVID-19 were shown to have a significant relationship with the explicit user interest which were Mouse Clicks and Page/Down strikes. Furthermore, the comparison between the two models showed that the second regression model achieved a higher accuracy level that is attributed to the common behavioral change imposed by the pandemic.
Design of a Novel Convolutional Deep Network Model for Car Accident Prediction
Authors: V Raviteja Kanakala Et al. 
Published: January 2024

Real-time collision risk estimation is thought to be essential to a sophisticated traffic management system. To swiftly determine accident probability is the goal of real-time crash risk prediction. However, due to the complex traffic situation on urban arterials, urban arterials were rarely included in previous studies, which mostly focused on highways. This paper suggests using Convolutional Deep Network model (CDNM) to forecast the probability of vascular accidents in real time. This model has the benefit of being able to use both LSTM and CNN. CNN retrieves the time-invariant characteristics, while LSTM captures the data's long-term dependability. To estimate the likelihood of an accident, many sorts of data are used, including weather, traffic, and signal timing data. There are also many other data preparation methods employed. The problem of data imbalance is also addressed by normalization which oversamples the crash cases. Using a variety of measures, the CDNM is enhanced on the training data and assessed on the test data. Five more benchmark models are constructed for model comparison. K-NN, ISVM, ANN, CNN, CNN-EVT and GAN are some of the models in this group. Experimental findings show that the proposed CDNM beats the competition in terms of sensitivity, specificity, accuracy, AUC and G-mean value. The findings of this paper demonstrate that CDNM can real-time prediction of crash risk at arterials.
Navigating Ethics in Digital Humanities: A Deep Dive into Decision Distribution within Higher Education at the University of Jordan
Authors: Imad Salah Et al. 
Published: January 2024

This research aims to aid higher education institutions in making decisions that align with student needs and enhance their satisfaction. It considers decision presentation, timing of implementation, and communication of the benefits tied to educational quality improvements. To gauge student opinions, an online questionnaire research design was adopted, involving 3,000 male and female students from the University of Jordan. Findings indicated that students generally express dissatisfaction with higher education decisions and regulations due to unclear communication and limited implementation time.
For predicting educational quality outcomes, four machine learning algorithms were employed, each corresponding to four different higher education decisions. Notably, the Random Forest (RF) algorithm showcased superior performance. In the initial questionnaire, it achieved an accuracy of 97%, which slightly decreased to 92% in the second questionnaire due to the expanded dataset and varying factors affecting accuracy. The k-Nearest Neighbors (KNN) algorithm also yielded impressive results, achieving a remarkable 94% accuracy in the third questionnaire.
In the third questionnaire, the Decision Tree (DT) algorithm exhibited an accuracy of 85% in optimal scenarios. In contrast, the Convolutional Neural Network (CNN) algorithm, tailored for intricate tasks with numerous variables, consistently performed below expectations across all questionnaires. Its efficacy consistently lagged alternative algorithms, indicating a misalignment with the specific demands of its operational framework.
A Study on Financial Performance of Selected Public Sector Banks
Authors: Balambigai Subramanian Et al. 
Published: January 2024

Public sector banks are those in which the government holds more than 50% of the total stock. The government formulates all the financial guidelines for public sector banks. The public sector banks operate under the government to inspire trust in the depositors that their money is safe. The aim of the study is to find out the financial performance of selected Public Sector Banks using ratio analysis. Tools used for the study Mean, F-test or ANOVA (Analysis of Variances). Conclusion of the study Public sector banks want to increase the management capability to increase the profits,ROE, EPS, ROI and to increase the efficiency of banks.