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We investigate a class of third-order nonlinear integro-differential equations (IDEs) with parallel computing of intelligent Internet of Things and wireless networks for numerical solutions. A numerical scheme based on the Haar wavelet has been established to compute the approximate solution for the problem under our consideration. By utilizing the mentioned tool, we discretize the involved derivatives and integrals. In this way, a sophisticated scheme is derived. Formulations for maximum root mean square and absolute errors have been given. Also, the convergent method has been discussed. In engineering, such as structural dynamics and control systems, third-order IDEs can improve modelling precision and solution effectiveness. Various examples have been testified by the aforementioned method. Additionally, by using different Gauss and collocation points (CPs), the aforementioned error terms were recorded. The convergence rate using distinct numbers of CPs has also been calculated, which is nearly equal to 2.

Herpes zoster (HZ) duplex symmetricus is an extremely rare variant of multidermatomal HZ where there is the involvement of bilaterally symmetrical dermatomes. This generally occurs in immuno-compromised individuals and is unusual in immunocompetent host. When occurring over the face, it can be associated with Ramsay Hunt syndrome. Bilateral symmetrical lesions present a unique diagnostic dilemma owing to morphological similarities with Eczema Herpeticum and rarely enteroviral infections. In such cases, correct interpretation of serological tests helps in the diagnosis of the condition. Here, we present the case of HZ duplex symmetricus occurring in an immune-competent individual over bilateral V1 3 and C4 dermatome (left) along with facial palsy.

As of 25th July, 2022, global Disease burden of 575,430,244 confirmed cases and over 6,403,511 deaths have been attributed to coronavirus disease 2019 (COVID-19). Co-infections/secondary infections continue to plague patients around the world as result of the co-morbidities like diabetes mellitus, biochemical changes caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) especially significant elevation in free iron levels, immune suppression caused by SARS-CoV-2, and indiscriminate use of systemic corticosteroids for the treatment of severe COVID-19 disease. In such circumstances, opportunistic fungal infections pose significant challenge for COVID-19 disease therapy in patients with other co-morbidities. Although COVID-19-associated Mucormycosis (CAM) has been widely recognized, currently extensive research is being conducted on mucormycosis. It has been widely agreed that patients undergoing corticosteroid therapy are highly susceptible for CAM, henceforth high index of screening and intensive care and management is need of an hour in order to have favorable outcomes in these patients. Diagnosis in such cases is often delayed and eventually the disease progresses quickly which poses added burden to clinician and increases patient load in critical care units of hospitals. A vast perusal of literature indicated that patients with diabetes mellitus and those with other co-morbidities might be highly vulnerable to develop mucormycosis. In the present work, the case series of three patients presented at Chest Disease Hospital Srinagar, Jammu and Kashmir infected with CAM has been described with their epidemiological data in supplementary section. All these cases were found to be affected with co-morbidity of Diabetes Mellitus (DM) and were under corticosteroid therapy. Furthermore, given the significant death rate linked with mucormycosis and the growing understanding of the diseases significance, systematic review of the literature on CAM has been discussed and we have attempted to discuss emerging CAM and related aspects of the disease.

Money laundering (ML) is a critical source of extracting the money illegally from the financial system. It is linked to various types of crimes, including corruption, exploitation of a specific community, drug use, and many others. Detection of ML operations is a difficult task on a global scale due to the large volume of financial transactions. However, it also allows criminals to use financial systems to carry out fraudulent transactions. It mainly concern minimizing the potentially risks associated with money laundering. Anti-money laundering-(AML) tools based on AI-driven applications are now tracking transactions to overcome this challenge. A total of 112 research papers are assessed to identify the literature's gaps and suggest new directions for the research area accordingly. The findings of this systemic literature review work will not only open new paths for the research community, but will also assist the state agencies in developing an optimal AML system to counter these major issues and provide a healthy environment for their residents. This article seeks to assess the existing situation from various angles and open up new pathways for future research directions to investigate and build high levels of authenticity and security in the financial industry using artificial intelligence (AI). [ABSTRACT FROM AUTHOR]

Rapid developments in the Internet of Things (IoT) have opened the door for game-changing applications in numerous sectors, especially the vehicle industry. There is a rising demand for efficient assessment and decision-making methodologies to pinpoint the most promising choices for the vehicle sector with the introduction of IoT-based maritime vehicles. To overcome this issue, the integrated multi-criteria decision-making analysis (MCDA) paradigm proposed in this research combines the additive ratio assessment (ARAS) and analytic hierarchy process (AHP) approaches to evaluate and choose IoT-based maritime vehicles based on their performance- and authenticity-related criteria in the vehicle sector. The selection issue is hierarchically organized, and the assessment criteria are prioritized using the AHP approach. There are seven performance and authentication related criteria are selected that might aid in the selection procedure. Using the AHP, we are assigned these criteria proportionate weights that reflect their respective significance and interrelationships. AHP, however, falls short of offering a thorough analysis of the alternatives that exist. To overcome these restrictions, this research presents the integration of AHP with the ARAS approach for the ranking of alternatives according to how well they perform against the set criteria. By using the ARAS technique, it is possible to get over the restrictions of AHP and achieve a more thorough assessment of maritime IoT-based vehicles. The efficiency of the framework is proven using empirical data and professional judgment. The findings show that the hybrid method successfully encapsulates the intricate relationships between the factors being evaluated and objectively appraises the potential of IoT-based maritime vehicles for the automotive sector. This study extends to the area by providing an organized and thorough method for assessing and choosing IoT-based maritime vehicles. Considering several factors and their mutual dependence, the hybrid AHP and ARAS technique gives decision-makers a powerful tool for evaluating the potential of IoT-based maritime vehicles in the automotive sector. Smart decisions on the deployment of IoT-based marine vehicles and maximizing the potential they present may be made by beneficiaries in the automotive sector using the study's results. [ABSTRACT FROM AUTHOR]

Introduction: Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) infection risks in co-morbid patients are still unknown two years after the pandemic began. The prevalence of antibodies against SARS-CoV-2 infection is crucial for determining disease preventive and mitigation strategies. Obesity, type 2 diabetes, and chronic cardiovascular disease can raise the risk of Coronavirus Disease-2019 (COVID-19), which has a greater morbidity and fatality rate. Aim: To determine the seroprevalence of SARS-CoV-2 (COVID-19) antibodies and their relationship to co-morbidities in Kashmir’s ethnic population. Material and Methods: The present observational cohort study was done in the Department of Pulmonary Medicine at Chest Disease Hospital Srinagar, Jammu and Kashmir, India, from September 2020 to September 2021 and 1,846 co-morbid unvaccinated patients were chosen for the study. As per standard methodology, a cohort study was undertaken, a questionnaire was prepared, and demographic and associated parameters were recorded. All participants had their immune profiles tested, and the existence of Immunoglobulin G (IgG) antibodies for SARS-CoV-2 was determined using the chemiluminisence immunoassay technique. Chi-square and Fischer exact test were used for stastical analyses and p-value

Abstract Background The data on medium-term follow-up of coronavirus disease-19 (COVID-19) pneumonia survivors is scarce. Medium-term follow-up will generate knowledge and help in devising a structured follow-up plan and to facilitate enrolment in clinical trials assessing the role of antifibrotic drugs in modifying the course of disease in order to avert long-term pulmonary sequelae of disease. The study was aimed to evaluate the lung findings on a medium-term follow-up (3 months or more) chest computed tomography (CT) in COVID-19 pneumonia survivors, assess the rate of resolution or persistence of lung abnormalities and to identify the initial demographic, clinical, and imaging characteristics that could potentially predict the persistence of lung abnormalities on follow-up. Results Out of the total study cohort of 81 patients, 46 (56.8%) demonstrated complete resolution of lung findings and the remaining 35 (43.2%) had residual lung opacities on follow-up CT. The most common type of residual abnormality was ground glass opacity (GGO) (16/35; 45.7%), followed by parenchymal bands (9/35; 25.7%), mixed pattern of GGO and parenchymal bands (6/35; 17.2%), bronchiectasis (6/35; 17.2%), and interlobular septal thickening (4/35; 11.4%). Patients with residual abnormalities were older, had higher BMI, more comorbidities, lower SpO2, longer hospital stay, higher rate of intensive care unit (ICU) admission, higher WBC count, a higher CT severity score, and lower rate of steroid administration with all p values < 0.05. Conclusion Nearly half of post-COVID-19 survivors had residual lung abnormalities after ≥ 3 months of follow-up. Certain clinico-radiological characteristics have the potential to identify the individuals at risk of having residual lung abnormalities on medium-term follow-up.

Immune hemolytic disease of the fetus and newborn is a condition in which there is the premature destruction of fetal or neonatal red cells due to maternal alloantibody against red cell antigen inherited from father. We report the case of Hemolytic disease of fetus and newborn (HDFN) due to anti-E and anti-Fya antibody. Blood grouping was done using the conventional tube technique. Rh and Kell phenotyping of the patient and her husband performed by column agglutination and Duffy phenotyping were done by the conventional tube technique. Direct antiglobulin test was performed by column agglutination technique. Antibody screening and identification were done. Anti-E and Anti-Fya were identified with titer of Anti-E: 1:64 and Anti-Fya: 1:256. The patient's neonate developed jaundice. Phototherapy was given. Bilirubin stabilized and discharged on the 12th day.

Background and Objectives: Crossmatching is one of the bases of pretransfusion testing. Resolving problems in crossmatching should be carried out after proper planning and following departmental guidelines, and hence that time is not wasted, or blood withheld from patient unnecessarily. The aim of this study was to find the prevalence and cause of incompatible crossmatch and to formulate root cause analysis to help ensure safe transfusion. Materials and Methods: This was a prospective study conducted at a tertiary care hospital in North India, from December 2018 to November 2019. Request for blood components was received along with 2 ml labeled sample in ethylenediaminetetraacetic acid and plain tube. Crossmatching was done by column agglutination method in polyspecific (IgG + C3d) bead cards by ortho clinical diagnostic using semi-automated biovue. In case of any incompatible result, it was resolved using appropriate steps. Results: During the study period from December 2018 to November 2019, only 67 (0.65%) of the 10,320 samples received were found to be crossmatch incompatible and evaluated and appropriate donor units issued. The crossmatch incompatibility was much higher in the females (46, 68.7%) than the males (21, 31.3%). The direct antiglobulin test (DAT) was positive in nine patient samples, and the indirect antiglobulin test was found positive in 37 incompatible crossmatch units. Eleven cases of incompatible crossmatch were due to wrong blood in tube, seven due to contamination of reagents, and three due to DAT-positive donor units. Conclusion: In this study, alloimmunization (55.2%) was the most prevalent cause of incompatible crossmatch, and the most common alloantibody identified was anti-E. Incompatible crossmatch poses a challenge in the field of transfusion medicine. Root cause analysis is a systemic method for identifying all the contributing factors to a problem, so that the corrective action can be taken. A logical stepwise approach will enable the provision of safe transfusion.

Introduction: Tuberculosis (TB) is an infectious bacterial disease caused by Mycobacterium tuberculosis, which most commonly affects the lungs. TB is one of the biggest public health challenges in the world especially in under-developing and developing countries. Aim: To determine the prevalence of TB in the Kashmir valley. Materials and Methods: The present cross-sectional study was conducted in the Department of Chest Medicine, Chest Diseases Hospital (CDH), Government Medical College, Srinagar and Intermediate Reference Laboratory (IRL), State TB Training and Demonstration Centre (STDC), CDH, Srinagar and in association with State TB Office (STO) Kashmir, India, from March 2019 to December 2020 in 10 districts. A total of 66,829 presumptive TB samples in 2019 inclusive of 450 samples of CDH and 63532 presumptive TB samples in 2020 inclusive of 400 samples of CDH were collected from Department of Chest Medicine, CDH, Government Medical College, Srinagar and Revised National TB Control Programme (RNTCP) Centres of Kashmir valley under State TB office Director Health Kashmir as per World Health Organisation (WHO) criteria. It included both the positive and negative cases of TB registered during the year 2019 and 2020. Results: The total prevalence of TB disease during the year 2019 was found to be 49.03 per 100,000 population and 37.31 per 100,000 population in the year 2020 respectively. There was no correlation among the surveyed demographics in the positive TB cases in the Kashmir valley (p>0.05). Srinagar city reported highest cases whereas Budgam and Pulwama reported least cases in the year 2019 and 2020. Conclusion: The respiratory precautionary measures like social distancing and use of face masks during the ongoing COVID-19 pandemic has reduced transmission and incidence of TB. Proper identification and treatment of infectious cases will prevent TB in the ethnic population. More studies are needed on large sample size.

Ethambutol is the primary commonly used antitubercular drug for the treatment of tuberculosis and atypical mycobacterial infection. Central nervous system toxicity other than optic neuropathy is not widely reported. We describe a case of a 46-year-old female with abdominal tuberculosis who developed mania after the commencement of ethambutol, and symptoms fully resolved with its cessation. She was prescribed rifampicin, pyrazinamide, isoniazid, and ofloxacin and has no further manic symptoms. This case supports that the patients being treated with ethambutol can develop mania which improves quickly within days of stopping it. A trial of antimanic drugs is needed for short-term rapid improvement of manic symptoms, whereas other anti-tubercular medications can be continued safely.

Background and Objectives: In women of reproductive age, leukocytosis is a risk factor that bridges low-grade chronic inflammation (metabolic inflammation), metabolic changes, and polycystic ovary syndrome (PCOS) and is a potential early predictor of PCOS. This study aims to explore the predictive role of quantitative changes in white blood cells (WBCs) and neutrophils in PCOS-associated metabolic changes. Materials and Methods: A total number of 176 blood samples were obtained from age-matched women of the reproductive period, comprising 88 PCOS cases and 88 healthy controls. Hematological, metabolic, and anthropometric indices and ultrasonic assessment were recorded. Results: Elevated levels of luteinizing hormone, testosterone, and lipid parameters except HDL-C levels, and the prevalence of metabolic syndrome in PCOS were statistically significant (p < 0.001). The neutrophil count and neutrophil–lymphocyte ratio (NLR) in PCOS patients were significantly higher (p < 0.001) than their counterparts. The predictive ability of the neutrophil count and neutrophil–lymphocyte ratio (NLR) for PCOS, and possibly its associating subclinical inflammation at optimum cut-off values for the neutrophil count and NLR of >46.62% (sensitivity 94.32% and specificity 74.42%) and >1.23 (sensitivity 71.59% and specificity 100%), respectively. With regard to the areas under the curve (AUC) and Youden indices, they constituted 0.922 and 0.697 for neutrophil count and 0.926 and 0.716 for NLR, respectively. The comparative ROC z-statistic value was 2.222 and a p = 0.026. The multiple linear regression analysis revealed no significant influence for hormonal and metabolic independent variables on the neutrophil count in PCOS cases, but, as can be expected, revealed a significant negative relationship with the other components of WBCs. Conclusion: In conclusion, relative neutrophilia and elevated NLR are potential cost-effective, sensitive, and specific predictors of PCOS that may also shed light on the mechanism of chronic low-grade inflammation that is characteristic of the disease.

Background: Frontline healthcare workers (FHCWs) are at an increased risk of contracting COVID-19. We aimed to assess the stress and psychological impact of the COVID-19 pandemic among FHCWs. Methods: This was an exploratory hospital-based study. A semistructured e-questionnaire was developed and shared through emails, WhatsApp groups, Facebook, and Twitter. The study instruments used were stress questionnaire and the impact of event scale—revised. Results: We received 133 valid responses. A total of 81 (61.4%) of the respondents were single, 74 (55.6) were male, 70 (52.6%) were between 20 and 29 years of age, and 91 (68.4%) were from urban background. A total of 83 (62.4%) of respondents were doctors and 28 (21.1%) were registered nurses. A total of 36 (27.1%) were posted in emergency and 34 (25.6%) were in the in-patient department. Feeling sad and pessimistic, feeling of being avoided by others, the burden of change in the quality of work, and worrying whether the family will be cared for in their absence were significantly more in nurses as compared to the doctors. Stress due to burden in an increase in the quantity of work was seen more in FHCWs working in the swab collection center as compared to those working in the in-patient department, emergency, or theaters. Severe psychological impact was seen in 81 (60.9%) of FHCWs. The psychological impact was significantly more in males and in those who were married. It was also significantly related to the place of posting. Conclusion: More than half of the FHCWs had a severe psychological impact owing to COVID-19. The psychological impact was more in males and those who were married, and it was related to the place of posting of the FHCWs. Nurses had significantly higher stress as compared to doctors.

BackgroundCoronavirus disease-19 (COVID-19) has not only spawned a lot of stigma and discrimination towards its survivors but also to their corpses. We aimed to assess the magnitude and correlates of stigma in these survivors, on return to their communities.MethodsThis was a cross-sectional, hospital-based, exploratory study conducted by the postgraduate department of psychiatry, in collaboration with the postgraduate department of chest medicine, Govt. medical college, Srinagar. The study was performed among COVID-19 survivors, who attended the outpatient department after their discharge from the hospital. Socio-demographic characteristics were recorded through semi-structured proforma. Stigma was measured by the stigma questionnaire. Data was analyzed using descriptive statistics and regression analysis.ResultsA total of 91 survivors consented to participate in the study. Almost half (46.2%) of them were in the age group of 30-49 years and close to two-thirds (68.1%) were males. About three-fourths (74.7%) were from the urban background. The mean time from hospital discharge to study entry was 11.7±5.1 [Range(R) = 7-21] days. 98% of survivors provided at least one stigma endorsing response and the total mean stigma score was 28.5±7.1[R = 6-39]. The mean stigma sub-scores were highest for enacted stigma (7.6±1.8) [R = 2-9] and externalized stigma (15.0±4.1) [R = 1-20]. Enacted stigma was significantly high in males as compared to females. Enacted stigma and internalized stigma were both associated with education. Enacted stigma, externalized stigma, disclosure concerns, and total stigma was significantly associated with the occupation. Being unemployed and time since discharge were identified as independent predictors of total stigma.ConclusionOur study results showed high levels of enacted and externalized stigma among COVID-19 survivors. Enacted stigma was more among males and in those who were highly educated. Survivor centered and community-driven anti-stigma programs are the need of the hour to promote the recovery and community re-integration of these survivors.

[This corrects the article DOI: 10.1371/journal.pone.0240152.].

BACKGROUND: Vaccine hesitancy is seen, globally, as a major factor that will determine future coronavirus disease-19 (COVID-19) spread and its effective management. This study aimed to identify COVID-19 vaccine perception, acceptance, confidence, hesitancy, and barriers among the general population. MATERIALS AND METHODS: This was an online survey which was developed and shared through social media platforms among the general population of Kashmir. The survey captured demographic data and used a validated hesitancy measurement tool. We analyzed the data using descriptive statistics and multivariable logistic regression using Stata 15 (Stata Corp. 2017. Stata Statistical Software: Release 15. College Station, TX, USA: Stata Corp LLC). RESULTS: A total of 835 responses were received. Most participants were males, with females compromising of 19.5% participants. 65.1% of participants were in the age group of 30–50, whereas 19.2% were below 30 years of age. 52.70% of respondents were willing to take the vaccine when available, while 32.5% of respondents were unsure about their decision of inoculation. The most cited reason for willingness to get vaccinated was an understanding of the disease and vaccination. 41.70% felt that the vaccines developed against COVID-19 have not been fully tested; therefore, concerns around the safety and its longer-term side effects were the reasons cited. Public health messaging should be tailored to address these concerns. CONCLUSIONS: Vaccine hesitancy is a global threat undermining the control of preventable infections. The government should take proactive steps to address the factors that may potentially impact the benefits expected from the introduction of a COVID-19 vaccine in the union territory.

BACKGROUND: The World Health Organization declared vaccine hesitancy as one of the planet's top 10 global health threats in 2019. With the rollout of the coronavirus disease-19 (COVID-19) vaccines, a survey was conducted to find out the hesitancy and the apprehensions that come along with taking COVID-19 vaccines among health-care workers (HCWs). MATERIALS AND METHODS: This was an online cross-sectional survey which was developed and shared through social media platforms among the HCWs of Kashmir. The survey captured demographic data and used a validated hesitancy measurement tool from January 2021 to February 2021. The data were analyzed by descriptive statistics and multivariable logistic regression using Stata 15 (Stata Corp. 2017. Stata Statistical Software: Release 15. College Station, TX: Stata Corp LLC). RESULTS: Willingness to take the COVID-19 vaccine when available was seen in 67.7% of the HCWs. Overall, 9.59% of respondents reported unwillingness to receive a vaccine for COVID-19, while 22.7% were unsure. The most commonly cited reason for willingness to get vaccinated was an understanding of the disease and vaccination, as reported by 81.5%. Being single was significantly related to an increased risk of vaccine hesitancy (adjusted odds ratio = 5.27, 95% confidence interval: 2.07–13.40). Among vaccine attitudes, concerns about the safety of the vaccine, unforeseen problems in children, and possible unknown future adverse effects of the vaccine were the most important determinants of unwillingness. CONCLUSIONS: A significant proportion of the HCWs showed vaccine hesitancy to the COVID-19 vaccine. Hesitancy attitudes were almost always driven by concern around the vaccine safety. States and health-care authorities need to recognize the massive trust deficit around the Covid-19 vaccine and use the popular media used by people to share credible and reliable information.

BACKGROUND: The coronavirus disease-19 (COVID-19) emerged from China and rapidly spread to many other countries all over the world. This study aimed to assess the prevalence of anxiety, depression, posttraumatic stress disorder, and obsessive–compulsive (OC) symptoms among COVID-19 survivors after their discharge from the COVID-19 treatment center. MATERIALS AND METHODS: This was a cross-sectional, hospital-based study performed among 119 COVID-19 survivors. The Hospital Anxiety and Depression Scale (HADS) was used to measure anxiety and depression. Posttraumatic Stress Disorder (PTSD)-Checklist (PCL) and Brief OC Scale were used to measure PTSD and OC symptoms. Data were analyzed by descriptive and inferential statistics using the SPSS (IBM Corp. Released 2015 version 23.0). RESULTS: The mean anxiety, depression, and PTSD scores were, 7.12 ± 0.68, 8.08 ± 0.22, and 19.78 ± 0.88, respectively. Based on cutoff scores, the prevalence of anxiety, depression, and PTSD among COVID-19 survivors was n = 53, 44.54%; n = 73, 61.34%; and n = 30, 25.21%, respectively. Older COVID-19 survivors (≥50 years) were more likely to show symptoms of depression and anxiety (P < 0.001) compared to younger ones. Furthermore, COVID-19 survivors who were ≥50 years of age experienced a greater level of PTSD compared to younger ones; similar trends were seen in those experiencing OC symptoms. In the present study, n = 98 (82.4%) were obsessed with fears of contamination and an equal number had compulsive handwashing. CONCLUSION: Anxiety, depression, PTSD, and OC symptoms are common among the COVID-19 survivors and that underscores the need to diagnose and manage mental health morbidities among these survivors long after their recovery from COVID-19.

Energy infrastructure is well thought-out to be one of the complex infrastructures due to its convoluted configuration and automatic control among all of the systems. With such systems, various connections are made for the purpose of configurations. The energy system infrastructure aims to analytically evaluate each element of the system based on fundamental energy branches according to the customer demand. Developing a novel critical evaluation approach for complex energy infrastructure is pertinent to the evaluation of mixed energy system infrastructure. Considering the functional relationships between elements and their infrastructures, a system is needed to overcome the limitations of the current systems. By doing the efficient modeling of enhancing the performance infrastructure of critical energy infrastructure enable better quantitative evaluation of system. The purpose of the proposed study is to develop an evaluation approach for enhancing the performance of critical energy infrastructure. With the help of the proposed approach, efficient multifeature model for enhancing the performance of critical energy infrastructure was experimentally calculated. The experimental setup of the proposed study was done in the Super Decision tool for an efficient multifeature model for enhancing the performance. Results of the experiments reveal the effectiveness of the proposed research., Author(s): Guangping Zhuo (corresponding author) [1]; Shah Nazir (corresponding author) [2]; Habib Ullah Khan [3]; Neelam Mukhtar [4] 1. Introduction Humans have become increasingly reliant on the computer infrastructure networks [...]

Context: The selection and use of appropriate multi-criteria decision making (MCDM) methods for solving complex problems is one of the challenging issues faced by decision makers in the search for appropriate decisions. To address these challenges, MCDM methods have effectively been used in the areas of ICT, farming, business, and trade, for example. This study explores the integration of machine learning and MCDM methods, which has been used effectively in diverse application areas. Objective: The objective of the research is to critically analyze state-of-the-art research methods used in intelligent decision support systems and to further identify their application areas, the significance of decision support systems, and the methods, approaches, frameworks, or algorithms exploited to solve complex problems. The study provides insights for early-stage researchers to design more intelligent and cost-effective solutions for solving problems in various application domains. Method: To achieve the objective, literature from the years 2015 to early 2020 was searched and considered in the study based on quality assessment criteria. The selected relevant literature was studied to respond to the research questions proposed in this study. To find answers to the research questions, pertinent literature was analyzed to identify the application domains where decision support systems are exploited, the impact and significance of the contributions, and the algorithms, methods, and techniques which are exploited in various domains to solve decision-making problems. Results: Results of the study show that decision support systems are widely used as useful decision-making tools in various application domains. The research has collectively studied machine learning, artificial intelligence, and multi-criteria decision-making models used to provide efficient solutions to complex decision-making problems. In addition, the study delivers detailed insights into the use of AI, ML and MCDM methods to the early-stage researchers to start their research in the right direction and provide them with a clear roadmap of research. Hence, the development of Intelligent Decision Support Systems (IDSS) using machine learning (ML) and multicriteria decision-making (MCDM) can assist researchers to design and develop better decision support systems. These findings can help researchers in designing more robust, efficient, and effective multicriteria-based decision models, frameworks, techniques, and integrated solutions. [ABSTRACT FROM AUTHOR]