Outbreak trends of fatality rate into coronavirus disease-2019 using deep learning

The World Health Organization (WHO) has declared the novel coronavirus as global pandemic on 11 March 2020. It was known to originate from Wuhan, China and its spread is unstoppable due to no proper medication and vaccine. The developed forecasting models predict the number of cases and its fatality rate for coronavirus disease 2019 (COVID-19), which is highly impulsive. This paper provides intrinsic algorithms namely - linear regression and long short-term memory (LSTM) using deep learning for time series-based prediction. It also uses the ReLU activation function and Adam optimiser. This paper also reports a comparative study on existing models for COVID-19 cases from different continents in the world. It also provides an extensive model that shows a brief prediction about the number of cases and time for recovered, active and deaths rate till January 2021.Copyright © 2023 Inderscience Enterprises Ltd.

Automated Healthcare System Using AI Based Chatbot

Medical care is vital to having a decent existence. Be that as it may, it is undeniably challenging to get an appointment with a specialist for each medical issue and due to the current global pandemic in the form of Coronavirus, the healthcare industry is under immense pressure to meet the ends of patients' needs. Doctors and nurses are working relentlessly to treat and help the patients in the best possible way and still, they face problems in terms of time management, technical resources, healthcare infrastructure, support staff as well as healthcare personnel. To resolve this problem, we have made a chatbot utilizing Artificial Intelligence (AI) that can analyze the illness and give fundamental insights regarding the infection by looking at the data of a patient who was previously counselled at a health specialist This will also assist in lessening the medical services costs. The chatbot is a product application intended to recreate discussions with human clients through intuitive and customized content. It is in many cases portrayed as the most moving and promising articulations of communication among people and machines utilizing Artificial Intelligence and Natural Language Processing (NLP). The chatbot stores the information in the data set to recognize the sentence and pursue an inquiry choice and answer the corresponding inquiry. Through this paper, we aim to create a fully functional chatbot that will help the patients/users to know about the disease by simply entering the symptoms they possess. Additionally, they can also get information about certain medicine by simply typing the name of the medicine. Another additional feature is the ability of the bot to answer general questions regarding healthcare and wellbeing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

High-performance association rule mining: Mortality prediction model for cardiovascular patients with COVID-19 patterns

On a global scale, 213 countries and territories have been affected by the coronavirus outbreak. According to researchers, underlying co-morbidity, which includes conditions like diabetes, hypertension, cancer, cardiovascular disease, and chronic respiratory disease, impacts mortality. The current situation requires for immediate delivery of solutions. The diagnosis should therefore be more accurate. Therefore, it's essential to determine each person's level of risk in order to prioritise testing for those who are subject to greater risk. The COVID-19 pandemic's onset and the cases of COVID-19 patients who have cardiovascular illness require specific handling. The paper focuses on defining the symptom rule for COVID-19 sickness in cardiovascular patients. The patient's chronic condition was taken into account while classifying the symptoms and determining the likelihood of fatality. The study found that a large proportion of people with fever, sore throats, and coughs have a history of stroke, high cholesterol, diabetes, and obesity. Patients with stroke were more likely to experience chest discomfort, hypertension, diabetes, and obesity. Additionally, the strategy scales well for large datasets and the computing time required for the entire rule extraction procedure is faster than the existing state-of-the-art method. © 2023 Faculty of Engineering, Alexandria University

Automated Healthcare System Using AI Based Chatbot

Medical care is vital to having a decent existence. Be that as it may, it is undeniably challenging to get an appointment with a specialist for each medical issue and due to the current global pandemic in the form of Coronavirus, the healthcare industry is under immense pressure to meet the ends of patients' needs. Doctors and nurses are working relentlessly to treat and help the patients in the best possible way and still, they face problems in terms of time management, technical resources, healthcare infrastructure, support staff as well as healthcare personnel. To resolve this problem, we have made a chatbot utilizing Artificial Intelligence (AI) that can analyze the illness and give fundamental insights regarding the infection by looking at the data of a patient who was previously counselled at a health specialist This will also assist in lessening the medical services costs. The chatbot is a product application intended to recreate discussions with human clients through intuitive and customized content. It is in many cases portrayed as the most moving and promising articulations of communication among people and machines utilizing Artificial Intelligence and Natural Language Processing (NLP). The chatbot stores the information in the data set to recognize the sentence and pursue an inquiry choice and answer the corresponding inquiry. Through this paper, we aim to create a fully functional chatbot that will help the patients/users to know about the disease by simply entering the symptoms they possess. Additionally, they can also get information about certain medicine by simply typing the name of the medicine. Another additional feature is the ability of the bot to answer general questions regarding healthcare and wellbeing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Impact on Stock Exchange Due To Covid-19 Using Apache Spark

Big data analytics is used to predict and analyze the data which is available in huge amount and having structured, unstructured and sometime semi structured values. Here in our research data analysis will be done on the behalf of data available which is nifty-50 stock market data. We are going to analyze the impact on nifty-50 due to covid-19. We have collected the dataset form Kaggle.com. The techniques used here is apache spark and language is Scala. In our research results will be shown on the basis of analysis done using the closing price and opening price of different stocks in different months and weeks. The results will be expressed in the form of graph using data visualization technique in tableau. © 2022 American Institute of Physics Inc.. All rights reserved.

A Phase II Study of Metformin with Pemetrexed/Carboplatin in Patients with Metastatic Non-Squamous Non-Small Cell Lung Cancer

Introduction: There is a subset of NSCLC patients ineligible for benefit from TKIs/Immunotherapy (e.g. STK11 mutation conferring resistance to Immunotherapy). Besides, many patients cannot afford these therapies. Metformin has anticancer properties acting both on glycolytic metabolism and tumor microenvironment. In vitro studies suggest synergism between metformin and pemetrexed. STK11 deficient cell lines are more sensitive to metformin. Clinical studies combining metformin with chemotherapy are limited by small sample size. We conducted an exploratory phase-2 clinical trial of metformin with pemetrexed/carboplatin in advanced non-squamous NSCLC. Methods: This was a single center, open label, single arm phase 2 clinical trial with a Simon’s two stage design. The null hypothesis was that the combination would not improve the 6-month PFS rate by 15%, from 50%. Treatment-naive, non-diabetic patients aged 18-75 years with NSCLC (adenocarcinoma/not-otherwise-specified) with stage IV disease having ECOG PS 0-2 with unmutated EGFR/ALK and without brain metastasis or with asymptomatic brain metastases were treated with pemetrexed-carboplatin chemotherapy and metformin for six months. The primary outcome was 6-month progression free survival (PFS) rate. Secondary outcomes were safety, overall survival (OS), overall response rate (ORR), proportion of STK 11 mutation and effect of STK 11 mutation on 6-month PFS rate. PFS and OS were estimated using the Kaplan-Meier method. Targeted sequencing was attempted for available tissue specimens. Results: The first interim analysis was performed after enrollment of 26 patients for the first stage (before the target accrual of first stage was reached) due to slow accrual, in view of COVID pandemic. The study was terminated after first stage for futility. The median age of patients in the study was 52 years (range, 30 to 68) and 18 patients (69.0%) were males. Half of the patients had ECOG-PS 2. Brain metastases were present in eight (31%) patients and among these four (50%) were symptomatic at presentation. The median follow-up time was 25 months. The median PFS was four months. 6-month PFS rate was 28% (95% CI - 0.12 to 0.46). Of the 25 evaluable patients, five (20%) had a partial response, and eight (32%) had stable disease;13 (52%) of the patients had disease control. The median OS was 16 months. During combined therapy, 14 (54%) and 3 (11%) patients had any grade and grade 3 anemia respectively. One patient had grade 3 neutropenia. Among non-hematological toxicities, gastrointestinal toxicities (nausea, vomiting and diarrhea) were the most common. No grade 4 toxicities were reported. There were no treatment discontinuations, however treatment delay due to grade three toxicities was present in two patients. Dose modification for Metformin was required in four patients. Targeted Sequencing was possible in nine cases. Two of these patients had STK11 mutation and an associated bad outcome (PFS < 2 months). Conclusions: We could not demonstrate the benefit of combination of Metformin with pemetrexed-carboplatin in terms of improvement in 6-month PFS rate. The addition of metformin to pemetrexed-carboplatin has an acceptable safety profile. Future trials should test metformin in specific subsets (STK11 mutated) and in combination with immunotherapy and TKIs. Keywords: Metformin, NSCLC, STK11

MISDIAGNOSIS OF KIKUCHI-FUJIMOTO DISEASE: THE IMPORTANCE OF TIMELY HISTOPATHOLOGY IN LYMPHADENITIS

Background: Febrile lymphadenopathy not responding to first line antibiotics in a patient hailing from or with a history of travel to tuberculosis endemic countries is often primarily diagnosed as extra-pulmonary tuberculosis. However, histiocytic necrotizing lymphadenitis or Kikuchi-Fujimoto Disease(KFD) presents with similar clinical features. Etiological theories of KFD include viral agents, autoimmunity, and physicochemical factors such as leaking implants. Although KFD has classically been described in young Asian females, recent studies show men and women can be equally affected, with cases increasingly being reported from the USA and Europe as well. Availability bias amongst physicians can lead to misdiagnoses, especially in patients from tuberculosis endemic countries. Objectives: To describe a case of misdiagnosis of KFD in an adolescent. Design/Method: Case report. Results: A 16-year-old male from a tuberculosis endemic country, with a history of asthma, eczema and excision of omental infarct, presented with sub-occipital lymphadenopathy which resolved with antibiotics. Six months later, he complained of tender left cervical lymphadenopathy, associated with fever and fatigue, which lasted for a month. Two courses of antibiotics failed to decrease symptoms. Based on his clinical history, he was started on empirical anti-tubercular medications despite negative tests for tuberculosis. However, his symptoms began to worsen after three weeks of this treatment, and he developed high evening rise of temperature associated with chills, night sweats, frontal headache, pedal edema and generalized pruritic maculopapular rash. Laboratory workups revealed leukopenia (WBC:3830/μL);elevated Erythrocyte sedimentation rate (29 mm/h), C-reactive protein (68.6 mg/dL), Aspartate Aminotransferase(95 U/L) and Alanine Aminotransferase(61 U/L). Rapid antigen test for SARS-CoV2 was negative, and no appreciable levels of SARS-CoV-2 IgG antibodies were detected. Investigations for Tuberculosis, EBV, CMV, Dengue, Malaria, Typhoid, Leptospirosis and Scrub typhus were all negative. Chest X-ray and abdomen ultrasound scan were normal. Histopathological analysis of the excised cervical lymph nodes demonstrated crescentic histiocytes and karyorrhexis in a background of coagulative necrosis. Neutrophils, granulomas and acid-fast bacilli were absent. Immunohistochemistry was positive for CD3, CD20, CD68;and negative for CD15, CD30 and PAX-5. A diagnosis of KFD was made, and patient was given supportive treatment only. His symptoms rapidly resolved within 48 hours, with complete resolution by three months. Conclusion: It is important to raise awareness of KFD, a benign and self-limiting condition with good prognosis, which has many clinical symptoms mimicking grave conditions like extra-pulmonary tuberculosis, SLE and lymphomas. Timely histopathological analysis can help avoid anxiety surrounding a misdiagnosis and adverse reactions due to unnecessary toxic treatments.

An audit of therapeutic anticoagulation with Anti-Xa monitoring during pregnancy

The risk of venous thromboembolism (VTE) increases by 10% in pregnancy to around 1/1000 and is a leading cause of death in pregnant women. Low molecular weight heparins (LMWHs) are the anticoagulant of choice for treatment of acute VTE during pregnancy. The initial dose of LMWH is weight based but currently there is lack of evidence supporting routine Anti-Xa monitoring during pregnancy and LMWH dose adjustments based on Anti-Xa levels. We conducted a retrospective audit of pregnant patients receiving therapeutic dose LMWH between October 2020 and October 2021 in a tertiary referral centre. The aim of this audit was to review LMWH dosages required in pregnancy to achieve peak Anti-Xa levels relative to weight-based and report maternal thrombotic or bleeding outcomes based on dose adjustments. A total of 21 pregnant patients were included who required therapeutic LMWH (Tinzaparin) during pregnancy. Of these, 10 (48%) had an acute VTE in the index pregnancy;one (4%) had recurrence of DVT despite weight adjusted LMWH. Ten (48%) were on long-term anticoagulation for a prior VTE including two with antithrombin deficiency and one with JAK 2 positive myeloproliferative disorder. They were all changed to LMWH during pregnancy. The site of acute VTE in index pregnancy (11) included: five (45%) deep vein thrombosis (DVT), three (28%) pulmonary emboli (PE), two (18%) had thromboses at an unusual site, and one patient (9%) had a superficial thrombophlebitis with gestational age range 7-40 weeks. Majority of pregnant patients (18/21;86%) had at least one peak Anti-Xa measured, and 12 (67%) patients had dose of LMWH increased at least once to achieve a target peak Anti-Xa level of 0.5-0.7 IU/ml. Five required two dose adjustments, and one required three dose adjustments. Nineteen patients have delivered and two have ongoing pregnancy. Twelve patients had spontaneous vaginal delivery, three assisted vaginal delivery and four had caesarean section for obstetric reasons. No patients had a recurrent thrombosis while on therapeutic dose LMWH and with dose adjustments as per peak anti-Xa level. One patient who presented with an acute DVT at 40 weeks of gestational age (GA) was managed with twice daily therapeutic dose Tinzaparin and insertion of an inferior vena cava (IVC) filter for anticoagulation interruption around delivery. The last dose Tinzaparin was 12 h prior to emergency Caesarean Section. She had postpartum haemorrhage with an estimated blood loss of 1800 ml but did not require blood product support and there was no evidence of progression of her symptoms of VTE or bleeding postoperatively when anticoagulation was resumed. Of note, six patients (29%) had a BMI >30 with five (83%) needing at least one adjustment of LMWH dose based on Anti-Xa levels and two (33%) needing > 2 dose increments with LMWH based on Anti-Xa monitoring. One patient had recurrence of PE on weight based LMWH dose with no recurrence of symptoms when the LMWH dose was adjusted to peak Anti-Xa level. None of the patients developed SARS-CoV-2 infection in the reported cohort. Fourteen (67%) pf pregnant had received their COVID-19 vaccination during this period . None of the thrombotic episodes were associated with COVID-19 vaccination. Although this audit study has limitations due to small patient numbers there was no evidence of increase in bleeding or thrombotic risk with ongoing anticoagulation with Anti-Xa monitoring during pregnancy..

Anti-Covi-Bot: A Robot to Combat COVID-19

As per researchers worldwide, the practice of social distancing and wearing masks may have to be continued till the end of 2022 or even after considering the deadly and unpredictable nature of the COVID-19 virus that has gripped our world. In spite of all the rules and regulations in place we still find people breaking the rules. Also, it is to be noted that many officials who are tasked with enforcing the rules have either been severely affected or have lost their lives. Our work focuses on creating a holonomic robot, that can detect violations to mask and social distancing rules and generate alerts. Our work uses OpenCV and CNN model for face mask detection and pre-trained Mobile-Net Single Shot Object Detection (SSD) model to detect people and check if social distancing is adhered to. © 2021 IEEE.

Assessment of Pulmonary Function Test in Patients Having Tuberculosis, Asthma and COPD

Introduction: Respiratory illnesses were supposed to be sore for humans but considering the present day scenario critical illnesses like COVID 19 are becoming leading cause of death.But never the less antiquity of respiratory ailments can always be remembered which was governing the world of illnesses over many decades and centuries. The deadlier variants of respiratory diseases like COPD, asthma and pneumonia are ranked at the third position in leading causes of death around the globe.For dominating these disease early detection, treatment, follow up and most importantly prevention plays the key role. For diagnosing respiratory diseases Pulmonary function tests along with clinical presentations should be of utmost priority. Thus a study was designed to see effects on lungs caused by COPD, asthma and Tuberculosis coming to our institution which may lay a stepping stone towards management of these deadlier diseases in future Material and Methods: 60 individuals were enrolled in this observational study. They were categorized into three groups. a) Tuberculosis (TB) b) Asthma, c) COPD patients. FEV1, FVC, FEV1/FVC were measured by spirometer. Results: The mean ± SD levels of FVC in asthma is (2.2 ± 0.66),TB (2.37 ± 0.78) and (1.5 ± 0.58) in COPD patients. The levels of FEV1 in respiratory diseases like Asthma, TB are ( 1.92 ± 0.55), (1.92 ± 0.64) and (1.09 ± 0.45) in COPD patients. The ratio between the FEV1/FVC in Asthma, TB and COPD are ( 88.0 ± 4.52, 81.11 ± 4.64, 64 ± 6.79) respectively. In Asthma and TB the FVC and FEV1 values are indifferent with no statistically significant of (p>0.05).While comparison betweenCOPD- asthma and COPD-TB patents the FEV1 and FVC levels were changedto be statistically significant (p <0.05). Data were analyzed by student ANOVA and Bonferronis post hoc test- P value < 0.05 can be considered statistically significant Conclusion: It can be concluded that Spirometry of the patients with diseases like TB, asthma and COPD can lay a stepping stone in diagnosis and treatment outcomes of the patients if performed well within time which can benefit the society by reducing the disease burden.

UTAUT3 model viability among teachers due to technological dynamism during COVID-19

Purpose: The purpose of this paper is to identify the viability of the extended Unified Theory of Acceptance and Use of Technology Model 3 (UTAUT3) model among the teachers especially during COVID-19 towards the use of technology. Design/methodology/approach: An extensive primary survey has been conducted through a well-structured tool under UTAUT3 model. The survey is conducted among 450 teachers from various institutions taken for the study. The data was collected from the Northern India. The data analysis will be done through the SmartPLS software with application of structural equation modelling (SEM). Findings: The results are strong for educators and policy makers. It was found that performance expectancy is positively related to the behavioural intentions among teachers. Teachers consider that usage of technology will boost their job and task performance. Practical implications: This study has a very strong implications in the field of education in case or replacement of traditional teaching patterns with modern one during pandemic times. It will be effective if teachers would prioritize their work. There will be more effective teaching and learning system in future. Originality/value: The study validates the constructs of UTAUT3 model in understanding teachers' behaviour and attitude towards technology acceptance. Furthermore, the study invites research from different viewpoint to investigate the role of UTAUT3 model in an individuals' behaviour and attitude towards technology acceptance. © 2021, Emerald Publishing Limited.

Hydroxychloroquine for SARS CoV2 prophylaxis in healthcare workers – A multicentric cohort study assessing effectiveness and safety

Background, Objective: We studied the effectiveness and safety of Hydroxychloroquine (HCQ) preexposure prophylaxis against COVID-19 in Healthcare workers (HCWs) previous studies being inconclusive due to small sample and lack of risk stratification Design and setting: Prospective, observational, multicenter cohort study in 44 hospitals in 17 Indian states during May-Sept 2020 Participants: 12089 Consenting Doctors, nurses, ancillary staff likely exposed to COVID-19 patients irrespective of whether taking HCQ preexposure prophylaxis (4257) or not(7826) participated,(in 6 data missing) Measurements: Data was collected on a self administered online questionnaire. Statistical analysis was done on SPSS version 20. Results: Age above 45 years, diabetes, hypertension, history of COVID contact were independent risk factors for COVID positivity. HCQ intake did not show an independent association. However, when adjusted for other risk factors, HCQ dose as per Government recommendations, 2-3, 4-5 and 6 or more weeks reduced the probability of COVID positivity by 34%, 48%, 72% respectively. COVID free median survival time was higher in non-diabetics, non-hypertensives, persons below 45 years, with no prior exposure to COVID case and those who took HCQ for more than 6 weeks With modeling extent of risk reduction under different scenarios of risk and HCQ intake was 1-65% . Major adverse events reported were GI disorder, palpitation, giddiness and 140 persons discontinued due to adverse events. Limitations: Limitation of self reporting by HCWs in online form, minimized by specified options,mandatory fields and telephonic verification Conclusion: The study examined individual risk factors including site variations and found that HCQ 800 mg loading followed by 400 mg weekly, dose for more than 2 weeks, reduced the risk of COVID-19, in HCWs, and is a useful option in low resource settings till vaccines are made accessible to all. © 2021 Journal of Association of Physicians of India. All rights reserved.

Numerical analysis approach for models of COVID-19 and other epidemics

The world wide spread of COVID-19 epidemic has instigated an unprecedented demand and supply of research related to this epidemic. Together with medical research for treatment and cure of the pandemic, efficient mathematical modeling of epidemics is the need of hour. First, this paper depicts a detailed and comprehensive compendium of various numerical methods used for infectious diseases modeling during last 14 years including the very recent work done for COVID-19 models. This gives researchers a good insight about past work done, present efforts and future scope of numerical analysis methods for epidemic modeling. Second, this paper also proposes its numerical analysis approach based on Adomian decomposition method (ADM) for generalized SEIR model of COVID-19. The proposed method is shown to give very accurate numerical results.

Role of machine learning and big data in healthcare for the prediction of epidemic diseases: A survey

Epidemic diseases are the contagious or infectious diseases which are possible to be spread into the entire country, and are defined as an outbreak that occurs and affects an exceptionally high proportion of the population. However, these infectious ailments if controlled beforehand by using trending technologies for the early prediction would not turn into mortality situations. With this view, this paper is summarising the research work by using machine learning and big data handling techniques for the early prediction of epidemic diseases. The epidemic diseases especially covered in this review are influenza, malaria and dengue ailment. The diseases are compared against machine learning models used and input data contemplated. An observation for the prediction of diseases found that same factors associated with searching techniques give different results for different locations;overall searches are showing diversity and dearth in data. Moreover, dearth of data will mitigate the accuracy. Copyright © 2021 Inderscience Enterprises Ltd.

Antimicrobials Overuse in COVID-19: A Silent Pandemic of Antimicrobial Resistance

The COVID-19 epidemic has presented many treatment challenges to physicians due to the lack of specific effective medicines. The COVID-19 pandemic is also witnessing irrational antibiotic use. The aggressive use of several antibiotics for treatment of COVID-19, and its complications may lead to another pandemic of Antimicrobial Resistance (AMR). Limiting unnecessary antibiotic use in viral infections, like COVID-19, should be emphasized in antimicrobial stewardship programs.

Combining Initial Radiographs and Clinical Variables Improves Deep Learning Prognostication in Patients with COVID-19 from the Emergency Department

Purpose: To train a deep learning classification algorithm to predict chest radiograph severity scores and clinical outcomes in patients with coronavirus disease 2019 (COVID-19). Materials and Methods: In this retrospective cohort study, patients aged 21-50 years who presented to the emergency department (ED) of a multicenter urban health system from March 10 to 26, 2020, with COVID-19 confirmation at real-time reverse-transcription polymerase chain reaction screening were identified. The initial chest radiographs, clinical variables, and outcomes, including admission, intubation, and survival, were collected within 30 days (n = 338;median age, 39 years;210 men). Two fellowship-trained cardiothoracic radiologists examined chest radiographs for opacities and assigned a clinically validated severity score. A deep learning algorithm was trained to predict outcomes on a holdout test set composed of patients with confirmed COVID-19 who presented between March 27 and 29, 2020 (n = 161;median age, 60 years;98 men) for both younger (age range, 21-50 years;n = 51) and older (age >50 years, n = 110) populations. Bootstrapping was used to compute CIs. Results: The model trained on the chest radiograph severity score produced the following areas under the receiver operating characteristic curves (AUCs): 0.80 (95% CI: 0.73, 0.88) for the chest radiograph severity score, 0.76 (95% CI: 0.68, 0.84) for admission, 0.66 (95% CI: 0.56, 0.75) for intubation, and 0.59 (95% CI: 0.49, 0.69) for death. The model trained on clinical variables produced an AUC of 0.64 (95% CI: 0.55, 0.73) for intubation and an AUC of 0.59 (95% CI: 0.50, 0.68) for death. Combining chest radiography and clinical variables increased the AUC of intubation and death to 0.88 (95% CI: 0.79, 0.96) and 0.82 (95% CI: 0.72, 0.91), respectively. Conclusion: The combination of imaging and clinical information improves outcome predictions. Supplemental material is available for this article.© RSNA, 2020.

Propositional aspect between apache spark and hadoop map-reduce for stock market data

Big data analytics is becoming tremendously popular in every field today. Everyday lots of data are being generated and analyzed using big data analytics tools and technique. Here the technology used is apache spark and language used is Scala. So, in this paper study is being done on the behalf of research done in stock market data using apache spark technique. Here the nifty-50 data is taken to analyze the impact due to covid-19. As it is being seen that Covid-19 has affected almost everything around the globe, so the purpose is to analyze its effect on stock market. Thereafter comparison is done between the techniques used to analyze that massive volume of stock exchange data. Here the comparative analysis between Hadoop maps-reduce and apache spark on the behalf of some important parameter is being done. That concludes which technique is better for the analysis of the stock exchange data. © 2020 IEEE.