A Cyber Risk Assessment Approach to Federated Identity Management Framework-Based Digital Healthcare System.

This paper presents a comprehensive and evidence-based cyber-risk assessment approach specifically designed for Medical Cyber Physical Systems (MCPS)- and Internet-of-Medical Devices (IoMT)-based collaborative digital healthcare systems, which leverage Federated Identity Management (FIM) solutions to manage user identities within this complex environment. While these systems offer advantages like easy data collection and improved collaboration, they also introduce new security challenges due to the interconnected nature of devices and data, as well as vulnerabilities within the FIM and the lack of robust security in IoMT devices. To proactively safeguard the digital healthcare system from cyber attacks with potentially life-threatening consequences, a comprehensive and evidence-based cyber-risk assessment is crucial for mitigating these risks. To this end, this paper proposes a novel cyber-risk assessment approach that leverages a three-dimensional attack landscape analysis, encompassing existing IT infrastructure, medical devices, and Federated Identity Management protocols. By considering their interconnected vulnerabilities, the approach recommends tailored security controls to prioritize and mitigate critical risks, ultimately enhancing system resilience. The proposed approach combines established industry standards like Cyber Resilience Review (CRR) asset management and NIST SP 800-30 for a comprehensive assessment. We have validated our approach using threat modeling with attack trees and detailed attack sequence diagrams on a diverse range of IoMT and MCPS devices from various vendors. The resulting evidence-based cyber-risk assessments and corresponding security control recommendations will significantly support healthcare professionals and providers in improving both patient and medical device safety management within the FIM-enabled healthcare ecosystem.

Pashto Handwritten Invariant Character Trajectory Prediction Using a Customized Deep Learning Technique.

Before the 19th century, all communication and official records relied on handwritten documents, cherished as valuable artefacts by different ethnic groups. While significant efforts have been made to automate the transcription of major languages like English, French, Arabic, and Chinese, there has been less research on regional and minor languages, despite their importance from geographical and historical perspectives. This research focuses on detecting and recognizing Pashto handwritten characters and ligatures, which is essential for preserving this regional cursive language in Pakistan and its status as the national language of Afghanistan. Deep learning techniques were employed to detect and recognize Pashto characters and ligatures, utilizing a newly developed dataset specific to Pashto. A further enhancement was done on the dataset by implementing data augmentation, i.e., scaling and rotation on Pashto handwritten characters and ligatures, which gave us many variations of a single trajectory. Different morphological operations for minimizing gaps in the trajectories were also performed. The median filter was used for the removal of different noises. This dataset will be combined with the existing PHWD-V2 dataset. Various deep-learning techniques were evaluated, including VGG19, MobileNetV2, MobileNetV3, and a customized CNN. The customized CNN demonstrated the highest accuracy and minimal loss, achieving a training accuracy of 93.98%, validation accuracy of 92.08% and testing accuracy of 92.99%.

Customer Analysis Using Machine Learning-Based Classification Algorithms for Effective Segmentation Using Recency, Frequency, Monetary, and Time.

Customer segmentation has been a hot topic for decades, and the competition among businesses makes it more challenging. The recently introduced Recency, Frequency, Monetary, and Time (RFMT) model used an agglomerative algorithm for segmentation and a dendrogram for clustering, which solved the problem. However, there is still room for a single algorithm to analyze the data's characteristics. The proposed novel approach model RFMT analyzed Pakistan's largest e-commerce dataset by introducing k-means, Gaussian, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) beside agglomerative algorithms for segmentation. The cluster is determined through different cluster factor analysis methods, i.e., elbow, dendrogram, silhouette, Calinsky-Harabasz, Davies-Bouldin, and Dunn index. They finally elected a stable and distinctive cluster using the state-of-the-art majority voting (mode version) technique, which resulted in three different clusters. Besides all the segmentation, i.e., product categories, year-wise, fiscal year-wise, and month-wise, the approach also includes the transaction status and seasons-wise segmentation. This segmentation will help the retailer improve customer relationships, implement good strategies, and improve targeted marketing.

MHD Double-Diffusive Carreau Fluid Flow through a Porous Medium with Variable Thermal Conductivity and Suction/Injection.

In this article, we consider the effects of double diffusion on magnetohydrodynamics (MHD) Carreau fluid flow through a porous medium along a stretching sheet. Variable thermal conductivity and suction/injection parameter effects are also taken into the consideration. Similarity transformations are utilized to transform the equations governing the Carreau fluid flow model to dimensionless non-linear ordinary differential equations. Maple software is utilized for the numerical solution. These solutions are then presented through graphs. The velocity, concentration, temperature profile, skin friction coefficient, and the Nusselt and Sherwood numbers under the impact of different parameters are studied. The fluid flow is analyzed for both suction and injection cases. From the analysis carried out, it is observed that the velocity profile reduces by increasing the porosity parameter while it enhances both the temperature and concentration profile. The temperature field enhances with increasing the variable thermal conductivity and the Nusselt number exhibits opposite behavior.

Green Synthesis of CeO2 Nanoparticles from the Abelmoschus esculentus Extract: Evaluation of Antioxidant, Anticancer, Antibacterial, and Wound-Healing Activities.

Metal oxide nanoparticles synthesized by the biological method represent the most recent research in nanotechnology. This study reports the rapid and ecofriendly approach for the synthesis of CeO2 nanoparticles mediated using the Abelmoschus esculentus extract. The medicinal plant extract acts as both a reducing and stabilizing agent. The characterization of CeO2 NPs was performed by scanning electron microscopy (SEM), X-ray diffraction (XRD), ultraviolet-visible spectroscopy (UV-Vis), and Fourier transform infrared spectroscopy (FTIR). The in vitro cytotoxicity of green synthesized CeO2 was assessed against cervical cancerous cells (HeLa). The exposure of CeO2 to HeLa cells at 10-125 µg/mL caused a loss in cellular viability against cervical cancerous cells in a dose-dependent manner. The antibacterial activity of the CeO2 was assessed against S. aureus and K. pneumonia. A significant improvement in wound-healing progression was observed when cerium oxide nanoparticles were incorporated into the chitosan hydrogel membrane as a wound dressing.

Xbee-Based WSN Architecture for Monitoring of Banana Ripening Process Using Knowledge-Level Artificial Intelligent Technique.

Real-time monitoring of fruit ripeness in storage and during logistics allows traders to minimize the chances of financial losses and maximize the quality of the fruit during storage through accurate prediction of the present condition of fruits. In Pakistan, banana production faces different difficulties from production, post-harvest management, and trade marketing due to atmosphere and mismanagement in storage containers. In recent research development, Wireless Sensor Networks (WSNs) are progressively under investigation in the field of fruit ripening due to their remote monitoring capability. Focused on fruit ripening monitoring, this paper demonstrates an Xbee-based wireless sensor nodes network. The role of the network architecture of the Xbee sensor node and sink end-node is discussed in detail regarding their ability to monitor the condition of all the required diagnosis parameters and stages of banana ripening. Furthermore, different features are extracted using the gas sensor, which is based on diverse values. These features are utilized for training in the Artificial Neural Network (ANN) through the Back Propagation (BP) algorithm for further data validation. The experimental results demonstrate that the projected WSN architecture can identify the banana condition in the storage area. The proposed Neural Network (NN) architectural design works well with selecting the feature data sets. It seems that the experimental and simulation outcomes and accuracy in banana ripening condition monitoring in the given feature vectors is attained and acceptable, through the classification performance, to make a better decision for effective monitoring of current fruit condition.

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture.

The integration of medical signal processing capabilities and advanced sensors into Internet of Things (IoT) devices plays a key role in providing comfort and convenience to human lives. As the number of patients is increasing gradually, providing healthcare facilities to each patient, particularly to the patients located in remote regions, not only has become challenging but also results in several issues, such as: (i) increase in workload on paramedics, (ii) wastage of time, and (iii) accommodation of patients. Therefore, the design of smart healthcare systems has become an important area of research to overcome these above-mentioned issues. Several healthcare applications have been designed using wireless sensor networks (WSNs), cloud computing, and fog computing. Most of the e-healthcare applications are designed using the cloud computing paradigm. Cloud-based architecture introduces high latency while processing huge amounts of data, thus restricting the large-scale implementation of latency-sensitive e-healthcare applications. Fog computing architecture offers processing and storage resources near to the edge of the network, thus, designing e-healthcare applications using the fog computing paradigm is of interest to meet the low latency requirement of such applications. Patients that are minors or are in intensive care units (ICUs) are unable to self-report their pain conditions. The remote healthcare monitoring applications deploy IoT devices with bio-sensors capable of sensing surface electromyogram (sEMG) and electrocardiogram (ECG) signals to monitor the pain condition of such patients. In this article, fog computing architecture is proposed for deploying a remote pain monitoring system. The key motivation for adopting the fog paradigm in our proposed approach is to reduce latency and network consumption. To validate the effectiveness of the proposed approach in minimizing delay and network utilization, simulations were carried out in iFogSim and the results were compared with the cloud-based systems. The results of the simulations carried out in this research indicate that a reduction in both latency and network consumption can be achieved by adopting the proposed approach for implementing a remote pain monitoring system.

Comparison of once a day rifaximin to twice a day dosage in the prevention of recurrence of hepatic encephalopathy in patients with chronic liver disease.

BACKGROUND: Rifaximin has been used for prevention of recurrence of hepatic encephalopathy in twice a day dosage. The drug is expensive and lower dising may be possible. OBJECTIVE: To determine the efficacy of rifaximin once a day dose in the prevention of hepatic encephalopathy (HE) in patients with liver cirrhosis as compared with twice daily dose of rifaximin. METHODS: This Randomized control trial was carried out at the Department of Gastroenterology and Hepatology, Shifa International Hospital, Islamabad, Pakistan from November 2012 to February 2014. Patients with known chronic liver disease with at least one episode of HE in the past were randomized to group A (rifaximin 550 mg OD) and group B (rifaximin 550 mg BD), after fulfilling the inclusion criteria. Each patient was followed for 6 months for any episode of HE. Patients in each group were identified for any breakthrough episode of encephalopathy during this period. Data were analyzed using SPSS version 16. Chi-squared test and t-test were applied where required to determine the significant difference between the two groups. RESULTS: There were a total of 306 patients: 128 patients in Group A while 178 in group B. Majority of patients (75.81%) had hepatitis C virus with mean age of 52.30 ± 9.92, MELD score 13.58 ± 8.3, and 55.22% were in Child-Pugh B. Eighty-one patients had an episode of HE during the study period. There were 27 patients in group A and 54 patients in group B with breakthrough episode of HE (P = 0.088). CONCLUSION: This study suggests that there is no significant difference in rifaximin once a day or twice daily dose in preventing HE.

Body composition analysis to determine gender specific physical fitness equations in a cohort of Saudi population.

OBJECTIVE: To determine an association between body composition analysis and physical fitness in the Saudi population and derive gender specific physical fitness equations. METHODS: A total of 530 healthy Saudi adults aged 15-72 years (mean 37.16±14.12 years) were enrolled in this study. Body composition analysis was assessed by bioelectrical impedance analysis (BIA), with a commercially available body analyzer according to standard protocols. RESULTS: Different body composition parameters, such as age, height, BSA (body surface area), obesity degree, body mass index (BMI), body fat mass (BFM) and percent body fat (% BF) contents were significantly different in males and females except weight which was non-significant (p=0.649). There was significant positive or negative correlation among different body composition parameters except weight with age in males and weight with age, height and BSA in females. In males, all the body composition characteristics contributed to the fitness score except BMI and BFM, while in females, the most significant effect was contributed by weight and BFM. Female body composition characteristics were strongly related to fitness score compared to males (R(2) = 93.8% vs R(2) = 78.5%). CONCLUSIONS: Different body composition parameters like BFM and %BF played an important role in determining physical fitness of healthy male individuals instead of BMI, weight and BSA, while in females weight was the best predictor of physical fitness.

Green process innovation, green product innovation, leverage, and corporate financial performance; evidence from system GMM.

Natural resource usage has produced various environmental challenges. Green process innovation has been considered a viable option that can help both industry and society. This study investigates the impact of green process innovation and green product innovation on corporate financial performance. We based our findings on a sample of 280 listed non-financial firms operating in South Asia. Information was gathered from firms' annual and CSR reports from 2012 to 2022. This study's data was analyzed using a two-step dynamic panel system GMM, correlation analysis, multicollinearity diagnostic tests, and descriptive statistics. Corporate financial performance is measured with ROA, ROE and Tobin's Q. Overall findings of the study show that green innovation has a significant positive impact on all measures of financial performance. Investing in the innovation of green products and green process can assist businesses in avoiding environmental concerns and regulatory penalties, while also assisting them in establishing new market prospects and achieving new levels of success with their green products. In addition, developing products that are friendly to the environment is tightly connected to expanding green competencies, promoting a company's green image, and improving the company's financial performance. Particularly useful for policymakers in developing countries, the study's findings can be used to introduce paradigm-shifting legislation and penalties that speed up business adoption of green process innovation.

Quasi-modified-Newton method-based selective harmonic elimination in cascaded H bridge inverters.

Multilevel converters have gained significant popularity in medium-voltage and high-power applications due to their numerous advantages over traditional two-level converters. These advantages include reduced harmonic distortion, improved efficiency, and lower stress on power semiconductors. Selective harmonic elimination (SHE) is a modulation method that can be employed with multilevel converters to achieve high-quality output voltage waveforms. In this work, an extension of Broyden's method, known as the Quasi-Modified Newton Method, is implemented for selective harmonic elimination and accurate calculation of switching angles for a wide range of modulation indices. The proposed method is applied to cascaded H bridge inverters operating at levels 5, 7, and 9. The method offers simplicity, reduced computational burden, and faster convergence, making it easily implementable, reducing total harmonic distortion (THD), and reducing RMSE and MAD errors. The paper includes simulation and experimental results that validate the accuracy and effectiveness of the proposed approach.

An enhanced pattern detection and segmentation of brain tumors in MRI images using deep learning technique.

Neuroscience is a swiftly progressing discipline that aims to unravel the intricate workings of the human brain and mind. Brain tumors, ranging from non-cancerous to malignant forms, pose a significant diagnostic challenge due to the presence of more than 100 distinct types. Effective treatment hinges on the precise detection and segmentation of these tumors early. We introduce a cutting-edge deep-learning approach employing a binary convolutional neural network (BCNN) to address this. This method is employed to segment the 10 most prevalent brain tumor types and is a significant improvement over current models restricted to only segmenting four types. Our methodology begins with acquiring MRI images, followed by a detailed preprocessing stage where images undergo binary conversion using an adaptive thresholding method and morphological operations. This prepares the data for the next step, which is segmentation. The segmentation identifies the tumor type and classifies it according to its grade (Grade I to Grade IV) and differentiates it from healthy brain tissue. We also curated a unique dataset comprising 6,600 brain MRI images specifically for this study. The overall performance achieved by our proposed model is 99.36%. The effectiveness of our model is underscored by its remarkable performance metrics, achieving 99.40% accuracy, 99.32% precision, 99.45% recall, and a 99.28% F-Measure in segmentation tasks.

Enhancing handwritten text recognition accuracy with gated mechanisms.

Handwritten Text Recognition (HTR) is a challenging task due to the complex structures and variations present in handwritten text. In recent years, the application of gated mechanisms, such as Long Short-Term Memory (LSTM) networks, has brought significant advancements to HTR systems. This paper presents an overview of HTR using a gated mechanism and highlights its novelty and advantages. The gated mechanism enables the model to capture long-term dependencies, retain relevant context, handle variable length sequences, mitigate error propagation, and adapt to contextual variations. The pipeline involves preprocessing the handwritten text images, extracting features, modeling the sequential dependencies using the gated mechanism, and decoding the output into readable text. The training process utilizes annotated datasets and optimization techniques to minimize transcription discrepancies. HTR using a gated mechanism has found applications in digitizing historical documents, automatic form processing, and real-time transcription. The results show improved accuracy and robustness compared to traditional HTR approaches. The advancements in HTR using a gated mechanism open up new possibilities for effectively recognizing and transcribing handwritten text in various domains. This research does a better job than the most recent iteration of the HTR system when compared to five different handwritten datasets (Washington, Saint Gall, RIMES, Bentham and IAM). Smartphones and robots are examples of low-cost computing devices that can benefit from this research.

Evaluation of a multiphase cascaded H-bridge inverter for induction motor operation.

Multi-phase systems are becoming more popular for applications requiring high power and precise motor control, even if single-phase AC power is still frequently utilized in households and some enterprises. While both systems have benefits over single-phase, there are trade-offs associated with each. Because of its balanced operation and effective power transfer, the three-phase (3-Φ) system is the most widely used multi-phase system. Nevertheless, different phase values can be investigated for particular applications where reducing torque ripple and harmonic content is essential. Using odd numbers of phases (such as 5-Φ) that are not multiples of three is one method. This design has the ability to reduce torque ripple by producing a more balanced magnetic field as compared with even-numbered phases. But adding more phases also makes the system design and control circuitry more complex. Systems with five phases (5-Φ) provide a compromise between performance and complexity. Applications such as electric ship propulsion, rocket satellites, and traction systems may benefit from their use. Nevertheless, choosing a multi-phase system necessitates carefully weighing the requirements unique to each application, taking into account elements like cost, power transmission, control complexity, and efficiency. The increasing popularity of electric vehicles and renewable energy technologies has led to the need for inverters in current electric applications. Conventional inverters provide square wave outputs, which cause the drive system to become noisy and cause harmonics. Multi-phase multilevel inverters can be used to enhance inverter functioning and produce an improved sinusoidal output. This study focuses on an induction motor drive powered by a five-phase multilevel cascaded H-Bridge inverter. With less torque and current ripples in the motor rotor, the power conversion harmonics are reduced and the switching components of the inverter are under less stress. However, in comparison to traditional inverters, it does require a greater number of legs. Because the switches needed for the cascaded H-Bridge inverter are less expensive in five-phase systems, they are favoured over higher phase orders. Furthermore, the suggested inverter removes 5th order harmonics, something that is not possible with traditional inverters. A five-phase induction motor appropriate for variable speed driving applications is also suggested by this research. Lastly, utilizing pulse width modulation (PWM) converters and an FPGA controller, an experimental study is carried out to assess the dynamic performance of the suggested induction motor drive. Particular attention is paid to the In-Phase Opposition Disposition (IPD) PWM technique.

Enhancement in the efficiency of heat recovery in a Williamson hybrid nanofluid over a vertically thin needle with entropy generation.

The purpose of the present research is to conduct an examination of entropy generation in a 2D magneto Williamson hybrid nanofluid flow that contains cobalt ferrite and titanium oxide nanoparticles and undergoes surface-catalyzed reactions through a thin vertical needle. The consequences of joule heating and viscous dissipation are considered to elaborate the features of heat transport. Further, the influence of thermal stratification, thermal radiation, and homogeneous-heterogeneous reaction is also taken into account. Through the application of appropriate similarity variables, the dimensionless system of coupled ordinary differential equations is achieved. The coupled system of equations is numerically solved by the usage of the bvp4c technique in the MATLAB algorithm. The current investigation also compared the existing outcomes with the available literature, which shows great harmony between the two. The consequences of the physical parameters are discussed graphically and with numerical data. It is worth noting that larger values of homogeneous reaction strength and the surface-catalyzed parameter diminish the concentration field. Further, the velocity distribution and their related momentum boundary layer thickness, diminishes with the enlargement of the Weissenberg parameter.

Morphological, Mineralogical, and Biochemical Characteristics of Particulate Matter in Three Size Fractions (PM10, PM2.5, and PM1) in the Urban Environment.

Air pollution in megacities is increasing due to the dense population index, increasing vehicles, industries, and burning activities that negatively impact human health and climate. There is limited study of air pollution in many megacities of the world including Pakistan. Lahore is a megacity in Pakistan in which the continuous investigation of particulate matter is very important. Therefore, this study investigates particulate matter in three size fractions (PM1, PM2.5, and PM10) in Lahore, a polluted city in south Asia. The particulate matter was collected daily during the winter season of 2019. The average values of PM1, PM2.5, and PM10 were found to be 102.00 ± 64.03, 188.31 ± 49.21, and 279.73 ± 75.04 µg m-3, respectively. Various characterization techniques including X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), and scanning electron microscopy (SEM) combined with energy-dispersive X-ray spectroscopy (EDX) were used. FT-IR and XRD techniques identified the minerals and compounds like quartz, peroxides, calcites and vaterite, feldspar group, kaolinite clay minerals, chrysotile, vaterite, illite, hematite, dolomite, calcite, magnesium phosphate, ammonium sulfate, calcium iron oxide, gypsum, vermiculite, CuSO4, and FeSO4. Morphology and elemental composition indicated quartz, iron, biological particles, carbonate, and carbonaceous particles. In addition, various elements like C, O, B, Mg, Si, Ca, Cl, Al, Na, K, Zn, and S were identified. Based on the elemental composition and morphology, different particles along with their percentage were found like carbonaceous- (38%), biogenic- (14%), boron-rich particle- (14%), feldspar- (10%), quartz- (9%), calcium-rich particle- (5%), chlorine-rich particle- (5%), and iron-rich particle (5%)-based. The main sources of the particulate matter included vehicular exertion, biomass consumption, resuspended dust, biological emissions, activities from construction sites, and industrial emissions near the sampling area.

Time fractional analysis of channel flow of couple stress Casson fluid using Fick's and Fourier's Laws.

This study aim to examine the channel flow of a couple stress Casson fluid. The flow is generated due to the motion of the plate at [Formula: see text], while the plate at [Formula: see text] is at rest. This physical phenomenon is derived in terms of partial differential equations. The subjected governing PDE's are non-dimensionalized with the help of dimensionless variables. The dimensionless classical model is generalized by transforming it to the time fractional model using Fick's and Fourier's Laws. The general fractional model is solved by applying the Laplace and Fourier integral transformation. Furthermore, the parametric influence of various physical parameters like Casson parameter, couple stress parameter, Grashof number, Schmidt number and Prandtl number on velocity, temperature, and concentration distributions is shown graphically and discussed. The heat transfer rate, skin friction, and Sherwood number are calculated and presented in tabular form. It is worth noting that the increasing values of the couple stress parameter [Formula: see text] deaccelerate the velocity of Couple stress Casson fluid.

Natural bio-convective flow of Maxwell nanofluid over an exponentially stretching surface with slip effect and convective boundary condition.

The under-consideration article mainly focuses an unsteady three-dimensional Maxwell bio-convective nanomaterial liquid flow towards an exponentially expanding surface with the influence of chemical reaction slip condition. The feature of heat transport is achieving in the existenceof convective boundary condition and variable thermal conductivity. With the help of similarity variables, the flow form of equations is turned into a nonlinear form of coupled ODEs. The numerical solutions are calculated by adopting bvp4c function of MATLAB. Impact of distinct characteristics on the temperature, velocity microorganism and concentration field is graphically evaluated. Moreover, physical quantities are observed via graphs and tabulated data in details. It has been seen by the observation that the involvement of unsteadiness parameter restricts the change of laminar to turbulent flow. Further, for increasing velocity slip parameter velocity component in both directions shows lessening behavior. The Nusselt number exhibits diminishing behavior for larger values of Deborah number, and it shows the opposite behavior for larger values of convective parameter.

Blasius-Rayleigh-Stokes Flow of Hybrid Nanomaterial Liquid Past a Stretching Surface with Generalized Fourier's and Fick's Law.

The effect of Stefan blowing on the Cattaneo-Christov characteristics of the Blasius-Rayleigh-Stokes flow of self-motive Ag-MgO/water hybrid nanofluids, with convective boundary conditions and a microorganism density, are examined in this study. Further, the impact of the transitive magnetic field, ablation/accretion, melting heat, and viscous dissipation effects are also discussed. By performing appropriate transformations, the mathematical models are turned into a couple of self-similarity equations. The bvp4c approach is used to solve the modified similarity equations numerically. The fluid flow, microorganism density, energy, and mass transfer features are investigated for dissimilar values of different variables including magnetic parameter, volume fraction parameter, Stefan blowing parameter, thermal and concentration Biot number, Eckert number, thermal and concentration relaxation parameter, bio-convection Lewis parameter, and Peclet number, to obtain a better understanding of the problem. The liquid velocity is improved for higher values of the volume fraction parameter and magnetic characteristic, due to the retardation effect. Further, a higher value of the Stefan blowing parameter improves the liquid momentum and velocity boundary layer thickness.

Sustainable production and waste management policies for COVID-19 medical equipment under uncertainty: A case study analysis.

Unfortunately, an abrupt corona-virus disease (COVID-19) outbreak brought a drastic change in human lives. Almost every sector of human-beings and their related activities are severely infected and affected by this COVID-19 pandemic. As days are passing, the impact of the COVID-19 epidemic is going to be more severe. The fundamental needs for personal protective equipment (PPEs) are rising drastically all over the world. In India, many non-pharmaceutical companies or organizations such as automobile companies are engaged in producing the PPEs at a very marginal rate. Thus this paper proposes a modeling and optimization framework for sustainable production and waste management (SPWM) decision-making model for COVID-19 medical equipment under uncertainty. To quantify the uncertainties among parameter values, we have taken advantage of the intuitionistic fuzzy set theory. A robust ranking function is presented to obtain a crisp version of it. Furthermore, a novel interactive intuitionistic fuzzy programming approach is developed to solve the proposed SPWM model. An ample opportunity to generate the desired solution sets are also depicted. The performance analysis based on multiple criteria such as savings from baseline, co-efficient of variations, and desirability degrees is also introduced. Practical managerial implications are also discussed based on the significant findings after applying to the real case study data-set. Finally, conclusive remarks and the future research direction are also addressed on behalf of the current contributing study.