Therapeutic Repurposing Approach: New Opportunity for Developing Drugs Against COVID-19

The coronavirus disease 2019 (COVID-19) pandemic initiated by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has encouraged the repurposing of various drugs to treat the morbidity, mortality, and extent of the disease. Nowadays, the COVID-19 pandemic is a major health concern as it has already affected the whole world in all aspects. Drug repurposing is considered a new potential strategy as it is a cost-effective and less time-consuming process to establish a new indication for existing drugs. The present chapter has focused on the pathophysiology of COVID-19 and the reuse of the drugs based on pharmacological mechanisms. In the literature, various drugs like favipiravir, lopinavir, ritonavir, arbidol, chloroquine, hydroxychloroquine, interferons, etc. have been reported for repurposing purposes against COVID-19. Most of them are effective in in vitro and clinical studies. Drugs act mainly on viral entry, viral replication, angiotensin-converting enzyme-2 (ACE2), inflammatory mechanisms, etc. Based on viral pathogenesis and the mechanism of drugs using in silico, in vitro, and clinical studies, repurposing medicines might be considered an excellent opportunity to cure COVID-19. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Smart Watch to Avoid Face Touching During Pandemic Condition

The most dangerous Coronavirus, COVID-19, is the source of this pandemic illness. This illness was initially identified in Wuhan, China, in December 2019, and currently sweeping the globe. The virus spreads quickly because it is so simple to transmit from one person to another. Fever is one of the obvious signs of COVID-19 and is one of its prevalent symptoms. The mucosal areas, such as the nose, eyes, and mouth, are among the most significant ways to catch this virus. In order to prevent and track the corona virus infection, this research suggests a face-touching detection and self-health report monitoring system. Their hygiene will immediately improve thanks to this system. In this pandemic circumstance, people use their hands in dirty environments like buses, trains, and other surfaces, where the virus can remain active for a very long time. With an accelerometer and a pulse oximeter sensor, this system alerts the user when they are carrying their hands close to their faces. © 2023 IEEE.

Uptake of vaccination in older Indian patients with cancer: A cross-sectional observational study

Background: Older patients with cancer are at a higher risk of invasive infections. Vaccination is an effective approach to decrease the mortality and morbidity associated with infections. Objective(s): Our primary objective was to evaluate the proportion of older patients with cancer who had received routine vaccinations against pneumococcal, influenza, and coronavirus disease 2019 (COVID-19). Our secondary objective was to identify the factors associated with vaccine uptake such as age, sex, education, marital status, comorbidities, and place of residence. Material(s) and Method(s): This cross-sectional observational study was conducted in the geriatric oncology outpatient clinic of the Department of Medical Oncology at the Tata Memorial Hospital, a tertiary care cancer hospital in Mumbai, India, from February 2020 to January 2023. We included all patients aged >=60 years who were evaluated in the geriatric oncology clinic during the study period and for whom the immunization details were available. The uptake of COVID-19 vaccine was calculated from March 2021 onwards, which was when the COVID-19 vaccine became available to patients aged >=60 years in India. Result(s): We enrolled 1762 patients;1342 (76.2%) were male. The mean age was 68.4 (SD, 5.8) years;795 (45%) patients were from the west zone of India. Only 12 (0.68%) patients had received the pneumococcal vaccine, and 13 (0.7%) had received the influenza vaccine. At least one dose of the COVID-19 vaccine had been taken by 1302 of 1562 patients (83.3%). On univariate logistic regression, education, marital status, geographic zone of residence, and primary tumor site were correlated with the uptake of COVID-19 vaccine. Factors associated with a greater COVID-19 vaccine uptake included education (up to Std 10 and higher vs. less than Std 10: Odds Ratio [OR], 1.46;95% confidence interval [CI], 1.07-1.99;P = 0.018, and illiterate vs. less than Std 10: OR, 0.70;95% CI, 0.50-0.99;P = 0.041), marital status (unmarried vs. married: OR, 0.27;95% CI, 0.08-1.08;P = 0.046, and widow/widower vs. married: OR, 0.67;95% CI, 0.48-0.94;P = 0.017), lung and gastrointestinal vs. head-and-neck primary tumors (lung cancer vs. head-and-neck cancer: OR, 1.60;95% CI, 1.02-2.47;P = 0.038, and gastrointestinal vs.head-and-neck cancer: OR, 2.18;95% CI, 1.37-3.42;P < 0.001), and place of residence (west zone vs. central India: OR, 0.34;95% CI, 0.13-0.75;P = 0.015). Conclusion(s): Fewer than 1 in 100 older Indian patients with cancer receive routine immunization with influenza and pneumococcal vaccines. Hearteningly, the uptake of COVID-19 vaccination in older Indian patients with cancer is over 80%, possibly due to the global recognition of its importance during the pandemic. Similar measures as those used to increase the uptake of COVID-19 vaccines during the pandemic may be beneficial to increase the uptake of routine vaccinations.Copyright © 2023 Cancer Research, Statistics, and Treatment.

Social Economic Impacts for Covid-19 Pandemics Using Machine Learning Based Optimization Algorithm

As the number of COVID-19 patients grows exponentially, not all cases are likely dealt with by doctors and medical professionals. Researchers will add to the fight against COVID-19 by developing smarter strategies to achieve accelerated control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus that causes disease. Proposed method suggests best ways to optimize protection and avoid COVID-19 spread. Big benefit of the hybrid algorithm is that COVID-19 is diagnosed and treated more rapidly. Pandemic diseases possibilities are handling with help of Computational Intelligence, using cases and applications from current COVID-19 pandemic. This work discusses data that can be analyzed based on optimization algorithm which provides betterCOVID-19 detection and diagnosis. This algorithm uses a machine learning model to decide how the hazard function changes concerning characteristics of potential methods to find parameters in optimization of machine learning model, which has in many cases been shown to be accurate for actual clinical datasets. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Computational Intelligence Against Covid-19 for Diagnosis Using Image Processing Techniques in Healthcare Sector

Coronavirus 2019 (COVID-19) medical images detection and classification are used in artificial intelligence (AI) techniques. Few months back, from the observation it is witnessed that there is a rapid increase in using AI techniques for diagnosing COVID-19 with chest computed tomography (CT) images. AI more accurately detects COVID-19;moreover efficiently differentiates this from other lung infection and pneumonia. AI is very useful and has been broadly accepted in medical applications as its accuracy and prediction rates are high. This paper is developed and aims to fight against corona through AI using computational intelligence in detecting and classifying COVID-19 using Densnet-121 architecture on chest CT images from a global diverse multi-institution dataset. Furthermore, data from clinics and images from medical applications improve the performance of the proposed approach and provide better response with practical applications. Classification performance was evaluated by confusion matrices followed by overall accuracy, precision, recall and specificity for precisely classifying COVID-19 against any condition. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

TOFACITINIB IN PSORIATIC ARTHRITIS -WELL TOLERATED DRUG - REAL LIFE EXPERIENCE

BackgroundTofacitinib a small molecule JAK- inhibitors has been approved for use in psoriatic arthritis (PSA) since 2017 while it has shown to be effective in the clinical trials real life data is sparse.With increase in use there has been growing concern about the safety profiles and adverse events which makes it all the more important to have real life data.ObjectivesTo review patient records who were treated with tofacitinib for psoriatic arthritis and to assess the tolerance and continuation rate and also assess the occurrence of adverse events like infections, coronary artery disease.MethodsAll PSA patients who were prescribed tofacitinib from JAN-2021 to JUNE 2022 with minimum of 6 months followup were included for analysis. Demographics, weight recordings, lab parameters and occurence of adverse events were noted.ResultsThere were a total of 71 patients who were prescribed tofacitinib out of which 46 are continuing and 25 have stopped during this period. The mean age was 47.25 (10.9)yrs the mean disease duration was 4.182 (4.474)yrs The reason for stopping tofacitinib was better(52%) followed inefficacy(24%), and miscellaneous(24%)reasons..When analysing before and after tofacitninb one thing whihc was striking is the significant weight gain among patients with minimum of 3.52(3.06) kg weight gain and this weight gain was consistent even in stopped patients.in comparing the lab parameters before and after tofacitininb there was a significant redcution in CRP,ESR,PLATELET COUNT Table 1 and a minimal but insginificant rise in liver enzymes within the physiological range.When compared to before and after tofacitinib there was increased occurence of fatigue(18.3%), minor infections(11.2%), Gastrointerstinal adverse events (11.2%), alopecia (11.2%), Itching(10.4%), headache(9.8%), UTI(5.6%), cough (4.2%), transaminitis(2.8%), covid(1.7%), zoster(1.4%) and CAD(1.4%).ConclusionTofacitinib in psoriatic arthritis is well tolerated with significant reduction in the inflammatory markers and weight gain but serious adverse events in lesser percentage eventhough it leads to significant weight gain.Table 1.PARAMTERSBeforeAfterP valueWeight70.15 (14.19)72.31 (14.24)0.000249ESR45.29 (28.26)35.23 (28.33)0.037CRP21.56 (16.38)10.72 (11.98)<.0001PLATELET COUNT332.92 (88.77)307.09 (88.18)0.0046SGOT30.33 (9.99)35.69 (19.92)0.125SGPT22.57 (12.96)27.98 (20.17)0.116Reference[1]Ly K, Beck KM, Smith MP, Orbai A-M, Liao W. Tofacitinib in the management of active psoriatic arthritis: patient selection and perspectives. Psoriasis (Auckl) [Internet]. 2019;9:97–107. Available from: https://doi.org/10.2147/PTT.S161453Acknowledgements:NIL.Disclosure of InterestsNone Declared.

COVID-19 repellent cloth

In this research work, for the first time, we have developed and demonstrated a COVID-19 repellent coating on cotton cloth that not only repels the virus but also most of the human body fluids (superhemophobic). The coating was tested in the BSL3 lab. The controlled experiments revealed no significant increase in the log viral particles on coated fabric compared to the uncoated surface, evidence that the coated fabric resisted the SARS-CoV-2 inoculum. Further, the coated cloth exhibited excellent dust-free nature and stain resistance against body fluids (blood, urine, bovine serum, water, and saliva aerosol). It also shows sufficient robustness for repetitive usage. The fabrication process for the developed COVID-19 repellent cloth is simple and affordable and can be easily scaled up for mass production. Such coating could be applied on various surfaces, including daily clothes, masks, medical clothes, curtains, etc. The present finding could be a mammoth step towards controlling infection spread, including COVID-19.

Impact of Internet of Health Things (IoHT) on COVID-19 Disease Detection and Its Treatment Using Single Hidden Layer Feed Forward Neural Networks (SIFN)

COVID-19 endemic has made the entire world face an extraordinary challenging situation which has made life in this world a fearsome halt and demanding numerous lives. As it has spread across 212 nations and territories and the infected cases and deaths are increased to 5,212,172 and 334,915 (as of May 22 2020). Still, it is a real hazard to human health. Severe Acute Respiratory Syndrome cause vast negative impacts economy and health populations. Professionals involved in COVID test can commit mistakes when testing for identifying the disease. Evaluating and diagnosing the disease by medical experts are the significant key factor. Technologies like machine learning and data mining helps substantially to increase the accuracy of identifying COVID. Artificial Neural Networks (ANN) has been extensively used for diagnosis. Proposed Single Hidden Layer Feedforward Neural Networks (SLFN)-COVID approach is used to detect COVID-19 for disease detection on creating the social impacts and its used for treatment. The experimental results of the proposed method outperforms well when compared to existing methods which achieves 83% of accuracy, 73% of precision, 68% of Recall, 82% of F1-Score. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Multilevel Thresholding of Grayscale Complex Crop Images using Minimum Cross Entropy

Crop image segmentation plays a key step in the field of agriculture. The crop images present near the environs have complex backgrounds and their grayscale histogram is mostly multimodal. Hence, multilevel segmentation of grayscale crop images may be helpful for better analysis. This paper proposed multilevel thresholding of grayscale crop images incorporated with minimum cross entropy as an objective function. The time complexity of this technique increases with the threshold levels. Hence, the coronavirus herd immunity optimizer (CHIO) has been amalgamated with the objective function. This technique improves the image's accuracy. The CHIO is a humanbased algorithm that separates the foreground and background efficiently with multiple thresholds value. The simulation has been performed on grayscale crop images. It is. compared with bacterial foraging algorithm (BFO), and beta differential algorithm (BDE) to validate the accuracy. The results validates that the proposed method outperforms BFO and BDE for grayscale crop images in terms of fidelity parameters. The qualitative and quantitative results evidence the proficiency of suggested method. © 2023 IEEE.

Hospitality & Tourism Research during (COVID-19) Pandemic: A Bibliometric Analysis

COVID-19 has changed the world forever in every imaginable aspect. Hospitality and Tourism has been one of the world's largest employers and key economic contributors. Hospitality and Tourism has been one of the worst-hit sectors due to the pandemic (COVID-19) worldwide. This has called upon the attention of many researchers worldwide. The main purpose of this study is to analyse the literature during 2019-2022, identify the most productive authors, most influential countries, most productive institution and journals also top-performing research articles and keyword analysis to know the research themes and trends focussing coronavirus in the fields of Hospitality and Tourism. The study also suggests the areas of future research to the researchers and policymakers and proposes solutions to contemporary issues. The study uses "biblioshiny” – an interface of R-package and VOSviewer for conducting bibliometric analysis that ameliorates the quality of review bereft of any subjective biasness. ©Copyright IJHTS.

Medicolegal Aspects during Mass Casualty Due to Road Traffic Accidents in the Era of Covid: A Tertiary Care Institutional Experience

Background: In March 2020, the world health organization declared COVID-19 a world wide pandemic. Countries introduced public health measures to contain and reduce its spread. The effect of mandated societal lockdown to reduce the transmission of corona virus disease 2019 (COVID-19) on road traffic accidents is not known. For this we performed an in-depth analysis singdata of emergency and trauma centre UPUMS, Saifai. As most of the manpower was involved in managing Covid patients directly or indirectly, it was a challenge to manage these mass casualty patients who require intensive care as well as Medicolegal documentation, record keeping, Consent for life saving procedures in absence of Relatives. Material(s) and Method(s): We reviewed data on total 2876 road traffic accident records in UPUMS, Saifai from January 1, 2020 through September 30, 2020. We treated March 20th as the first day of mandated societal lock down and 1st July as the first day of re-opening. Result(s): We have found that the reis increase in road traffic accidents resulting in serious or fatal injuries during lockdown and post-lockdown period. There was increased Medicolegal burden in spite of the decreased medical resources, manpower as most of manpower and resources were being utilized for covid patients. Conclusion(s): Road traffic accidents are a prominent contributor to hospitalization and may negatively impact the existing hospital resources directed towards COVID-19.Copyright © 2023, World Informations Syndicate. All rights reserved.

Water's Cross-Cutting Nature in Achieving Sustainable Development Goals

Water is fundamental to sustainable development, a natural resource integral to the continued existence of all living species. Unfortunately, it is also the resource which has been most widely misused, mismanaged, and driven the global community to a water crisis, through its unfettered, irresponsible exploitation. The author's attempt through this chapter is to present the cross-cutting role that water plays in fulfilling achievement of all 17 Sustainable Development Goals by 2030. These ambitious goals are ideals for any civilized society in an attempt to eradicate poverty and ensure food security, health, education, gender parity, livelihood generation, peace, and justice. The author underscores the significance of water as the connector to ultimately arrive at the successful completion of all targets set in 2015, over a period of 15 years. As we recover from the global Covid-19 pandemic, the need for sustainable, renewable, and clean water and energy has been highlighted by international bodies. The author presents the case study of Meghalaya State in North-East India as an example of integrated synergies among water, food, livelihood, education, gender equality, and ecosystem restoration to achieve peace, partnership, and prosperity. India Water Foundation in collaboration with Meghalaya Basin Development Authority ideated upon what is now the Integrated Basin Development Livelihood Promotion Programme (IBDLP) that transformed a subsistence-based community into entrepreneurs, empowered with entrepreneurial capacity as they took ownership of their lands for optimum, sustainable utilization. This serves as an example for regional South-South cooperation and implementation in neighboring states through localization and ecosystem-based adaptation. © Springer Nature Switzerland AG 2021.

Scientific rationale of Indian AYUSH Ministry advisory for COVID-19 prevention, prophylaxis, and immunomodulation

The current outbreak of COVID-19 is caused by the SARS-CoV-2 virus that has affected > 210 countries. Various steps are taken by different countries to tackle the current war-like health situation. In India, the Ministry of AYUSH released a self-care advisory for immunomodulation measures during the COVID-19 and this review article discusses the detailed scientific rationale associated with this advisory. Authors have spotted and presented in-depth insight of advisory in terms of immunomodulatory, antiviral, antibacterial, co-morbidity associated actions, and their probable mechanism of action. Immunomodulatory actions of advised herbs with no significant adverse drug reaction/toxicity strongly support the extension of advisory for COVID-19 prevention, prophylaxis, mitigations, and rehabilitation capacities. This advisory also emphasized Dhyana (meditation) and Yogasanas as a holistic approach in enhancing immunity, mental health, and quality of life. The present review may open-up new meadows for research and can provide better conceptual leads for future researches in immunomodulation, antiviral-development, psychoneuroimmunology, especially for COVID-19.Copyright © 2021, Institute of Korean Medicine, Kyung Hee University.

COVID-19 Detection on X-Ray Image Using Deep Learning

COVID-19 is one of the threats that came out of nowhere and literally shook the entire world. Various prediction techniques have been invented in a very short time. This study also develops a Deep Learning (DL) model which can predict the presence of COVID-19 and pneumonia by analyzing the X-ray images of human lungs. From Kaggle, a collection of X-ray images of the lungs is collected. Then, this dataset is preprocessed using two alternative methods. Some of the techniques include image enhancement and picture resizing. The two deep-learning models are then trained using the preprocessed dataset. A few more examples of DL algorithms include MobileNet and Inception-V3. The best model is then selected by validating the learned deep-learning models. As the epochs count increases during training and validation, the accuracy value for both models increases. The value of the loss increases as the number of epochs decreases. During the fourteenth validation period, the model generates a loss value of 0.32 for the MobileNet technique. During the first few training epochs, accuracy is lower, and by the fifteenth, it is close to 0.9. The Inception-V3 method produces a loss value of 0.1452 at the eleventh validation epoch, which is the lowest value. The greatest accuracy value of 0.9697 is obtained after the twelfth cycle of validation. The model that performs better and has lower loss values is then put through one last test. Inception-V3 is therefore selected as the top method for COVID-19 detection. The Inception-V3 system properly predicted each of the normal images and the COVID-19 images in the final test. Regarding pneumonia, it correctly predicted just one image out of 20 that are so small as to be disregarded. When a patient cannot afford to find a doctor for consultation, the DL model created in this work can be utilized as a preliminary test for COVID-19. By including the model created in this study as a backend processor for a website or software application, the study's findings can be updated. © 2023 IEEE.

Are non-fungible token coins a good hedge against the stock market volatility?

We test the hedge property of non-fungible token (NFT) coins against equity market fluctuations and compare it with the hedge property of Bitcoin. We employ daily the returns of Bitcoin;three NFT coins, namely Theta, Enjin Coin and Decentraland, and three equity market indices: S&P 500, NASDAQ and CAC 40, ranging from 18 January 2018 to 12 January 2021. We estimate the hedge effectiveness of the three NFT coins and Bitcoin against stock market fluctuations. Our results suggest that NFT coins are a better hedge against equity market fluctuations than Bitcoin.

A Temperature-Modified Dynamic Embedded Topic Model

Topic models are natural language processing models that can parse large collections of documents and automatically discover their main topics. However, conventional topic models fail to capture how such topics change as the collections evolve. To amend this, various researchers have proposed dynamic versions which are able to extract sequences of topics from timestamped document collections. Moreover, a recently-proposed model, the dynamic embedded topic model (DETM), joins such a dynamic analysis with the representational power of word and topic embeddings. In this paper, we propose modifying its word probabilities with a temperature parameter that controls the smoothness/sharpness trade-off of the distributions in an attempt to increase the coherence of the extracted topics. Experimental results over a selection of the COVID-19 Open Research Dataset (CORD-19), the United Nations General Debate Corpus, and the ACL Title and dataset show that the proposed model - nicknamed DETM-tau after the temperature parameter - has been able to improve the model's perplexity and topic coherence for all datasets.

A Situational Based Reliability Indices Estimation of ULT Freezer using Preventive Maintenance under Fuzzy Environment

Reliability of high demand machines is quite necessary and it can be maintained through proper and timely maintenance, Ultra-low temperature (ULT) freezer is one of those kinds of machines which are in high demand during covid-19 pandemic for the storage of vaccine. The rapid production of vaccines for the prevention of coronavirus disease 2019 (COVID-19) is a worldwide requirement. Now the next challenge is to store the vaccine in a ULT freezer. It's become really a big problem to store the vaccine which creates the demand of ULT freezer. The present paper investigates a situational based performance of the ULT freezer with the aim to predict the impact of different component failures as well as human errors on the final performance of the same. For the study, it is not possible to extract the parameters (failure rate and repair time) of the components that never failed before. Thus, to overcome this difficulty, here authors use the possibility theory. Authors present the available data in Right triangular fuzzy number with some tolerance as suggested by system analyst. The lambda-tau methodology and arithmetic operations on right triangular generalized fuzzy numbers (RTrFN) are used to find the various performance parameters namely MTTF, MTTR, MTBF, reliability, availability, maintainability (RAM) and ENOF, under fuzzy environment. The proposed model has been studied using possibility theory under working conditions, preventive maintenance as well as under the rest of conditions. This study reveals the most and least critical component of the ULT freezer which helps maintenance department to plan the maintenance strategy accordingly.

Case report on meningoenceplapathy in an Indian patient post CoViD vaccination

The SARS-COV-2 infection-related severe illness is prevented by vaccinations. Therefore, it is relevant to report a case of post vaccine meningoencephalitis in a 30 year old male Indian patient, who presented with weakness in all the extremities, episodes of loose stool, fever, vomiting, tachypnea and loss of consciousness immediately following the 2nd dose of the COVID vaccination (COVAXIN).

Antiviral activity of the human endogenous retrovirus-R envelope protein against SARS-CoV-2

Coronavirus-induced disease-19 (COVID-19), caused by SARS-CoV-2, is still a major global health challenge. Human endogenous retroviruses (HERVs) represent retroviral elements that were integrated into the ancestral human genome. HERVs are important in embryonic development as well as in the manifestation of diseases, including cancer, inflammation, and viral infections. Here, we analyze the expression of several HERVs in SARS-CoV-2-infected cells and observe increased activity of HERV-E, HERV-V, HERV-FRD, HERV-MER34, HERV-W, and HERV-K-HML2. In contrast, the HERV-R envelope is downregulated in cell-based models and PBMCs of COVID-19 patients. Overexpression of HERV-R inhibits SARS-CoV-2 replication, suggesting its antiviral activity. Further analyses demonstrate the role of the extracellular signal-regulated kinase (ERK) in regulating HERV-R antiviral activity. Lastly, our data indicate that the crosstalk between ERK and p38 MAPK controls the synthesis of the HERV-R envelope protein, which in turn modulates SARS-CoV-2 replication. These findings suggest the role of the HERV-R envelope as a prosurvival host factor against SARS-CoV-2 and illustrate a possible advantage of integration and evolutionary maintenance of retroviral elements in the human genome.Copyright © 2023 The Authors.

Data Visualization for Developing Effective Performance Dashboard with Power BI

Data visualization is a very important step in data analysis as it provides insight into the data in a more effective manner that is interesting, simple, and understandable to every-one without any language barrier. It can also represent a huge amount of data in a small space very easily. In the previous two years, the whole world has suffered from a very terrifying nightmare known as COVID-19. Known to be starting from the country of China, the pandemic affected not only the health and well-being of mankind, but also had serious impacts on the economies of various countries. Hence, a visualization of the data set of the pandemic might provide beneficial insights for finding a possible solution and can help in overcoming the impacts of the pandemic. Microsoft Power BI is a very famous tool for analyzing data. Power BI provides a different way to visualize the data. This paper has been analyzed the covid-19 data by using Power BI to understand the trends and patterns of the Pandemic. With the help of visualizing the data, it can be represented in stacked column charts, tables, and maps. These three ways are easy and simple to understand the patterns of the pandemic. It also helps to understand how covid impact the world. This research with power BI dashboard by using a dashboard feature that connects different pieces of visual graphs. © 2023 IEEE.