CuO–TiO_(2) based self-powered broad band photodetector

An efficient room-temperature self-powered,broadband(300 nm–1100 nm)photodetector based on a CuO–TiO_(2)/TiO_(2)/p-Si(100)heterostructure is demonstrated.The CuO–TiO_(2)nanocomposites were grown in a two-zone horizontal tube furnace on a 40 nm TiO_(2)thin film deposited on a p-type Si(100)substrate.The CuO–TiO_(2)/TiO_(2)/p-Si(100)devices exhibited excellent rectification characteristics under dark and individual photoillumination conditions.The devices showed remarkable photo-response under broadband(300–1100 nm)light illumination at zero bias voltage,indicating the achievement of highly sensitive self-powered photodetectors at visible and near-infrared light illuminations.The maximum response of the devices is observed at 300 nm for an illumination power of 10 W.The response and recovery times were calculated as 86 ms and 78 ms,respectively.Moreover,under a small bias,the devices showed a prompt binary response by altering the current from positive to negative under illumination conditions.The main reason behind this binary response is the low turn-on voltage and photovoltaic characteristics of the devices.Under illumination conditions,the generation of photocurrent is due to the separation of photogenerated electron-hole pairs within the built-in electric field at the CuO–TiO_(2)/TiO_(2)interface.These characteristics make the CuO–TiO_(2)/TiO_(2)broadband photodetectors suitable for applications that require high response speeds and self-sufficient functionality.

Software Defect Prediction Using Hybrid Machine Learning Techniques: A Comparative Study

When a customer uses the software, then it is possible to occur defects that can be removed in the updated versions of the software. Hence, in the present work, a robust examination of cross-project software defect prediction is elaborated through an innovative hybrid machine learning framework. The proposed technique combines an advanced deep neural network architecture with ensemble models such as Support Vector Machine (SVM), Random Forest (RF), and XGBoost. The study evaluates the performance by considering multiple software projects like CM1, JM1, KC1, and PC1 using datasets from the PROMISE Software Engineering Repository. The three hybrid models that are compared are Hybrid Model-1 (SVM, RandomForest, XGBoost, Neural Network), Hybrid Model-2 (GradientBoosting, DecisionTree, LogisticRegression, Neural Network), and Hybrid Model-3 (KNeighbors, GaussianNB, Support Vector Classification (SVC), Neural Network), and the Hybrid Model 3 surpasses the others in terms of recall, F1-score, accuracy, ROC AUC, and precision. The presented work offers valuable insights into the effectiveness of hybrid techniques for cross-project defect prediction, providing a comparative perspective on early defect identification and mitigation strategies. .

HV Process Model of Software Development

Software Development Life Cycle (SDLC) is one of the major ingredients for the development of efficient software systems within a time frame and low-cost involvement. From the literature, it is evident that there are various kinds of process models that are used by the software industries for the development of small, medium and long-term software projects, but many of them do not cover risk management. It is quite obvious that the improper selection of the software development process model leads to failure of the software products as it is time bound activity. In the present work, a new software development process model is proposed which covers the risks at any stage of the development of the software product. The model is named a Hemant-Vipin (HV) process model and may be helpful for the software industries for development of the efficient software products and timely delivery at the end of the client. The efficiency of the HV process model is observed by considering various kinds of factors like requirement clarity, user feedback, change agility, predictability, risk identification, practical implementation, customer satisfaction, incremental development, use of ready-made components, quick design, resource organization and many more and found through a case study that the presented approach covers many of parameters in comparison of the existing process models. .

Prevention and treatment of hypoxia-induced colorectal cancer damage in albino Wister rats

Background:In the early metastasis of colon cancer,cancer cells detach,migrate,and infiltrate surrounding tissues,including lymph vessels and blood vessels.Tumor heterogeneity arises from both tumor cells and distinct microenvironments.Maldistribution of blood vessels,creates hypoxic regions within the tumors,fostering cancer stem cell-like properties due to reduced oxygen and nutrient supply.Under hypoxia,tumor cells shift to a glycolytic pathway,producing more lactic acid that acidifies the microenvironment and leads to unstable heart rate variability(HRV)factors,weight disparity,and a higher incidence of aberrant crypt foci(ACF).These hypoxic-induced parameters promote cancer cell invasion,increase radiation resistance,and facilitate cancer cell migration.Methods:In this study,we induced hypoxia-preneoplastic colon damage in albino Wister rats by administrating 1,2-dimethyl hydrazine(DMH).After successfully creating a hypoxic environment in albino Wister rats,resulting in preneoplastic colon damage,we randomly allocated Wistar albino rats into seven groups,each containing 8 animals,and conducted a 6-week study.Group 1-Normal control(administered 1 mM EDTA+saline,2 ml/kg/day,p.o.);group 2-Toxic control(administered DMH,30 mg/kg/week,s.c.);group 3-Standard treatment(DMH,30 mg/kg/week,s.c.for 6 weeks),followed by 5-fluorouracil and Leucovorin(25 mg/kg each on 1^(st),3^(rd),7^(th),and 10^(th) days,i.p.after 6 weeks administration of DMH);group 4-Low dose of P1(DMH,30 mg/kg/week,s.c.+P1,2 mg/kg,i.v.,weekly for 3 weeks);group 5-High dose P1(DMH,30 mg/kg/week,s.c.+P1,4 mg/kg,i.v.,weekly for 3 weeks),group 6-Low dose of P2(DMH,30 mg/kg/week,s.c.,+P2,2 mg/kg,i.v.,weekly for 3 weeks),group 7-High dose of P2(DMH,30 mg/kg/week,s.c.,+P2,4 mg/kg,i.v.weekly for 3 weeks).Results:DMH-treated rats exhibited alterations in HRV factors,weight disparity,elevated gastric pH,increased total acidity,a higher incidence of ACF,and changes in antioxidant markers(TBARs,SOD,catalase,GSH).Brightfield microscopy at 40x magnification revealed the presence of large crypts within aberrant crypt foci in the toxic control group.Conclusion:Treatment groups P1 and P2 containing triazine derivatives initiated proteasomal degradation of Hypoxia Inducible Factor-1α(HIF-1α)by activating Prolyl Hydroxylase(PHDs)pathways.HIF-1αunder a hypoxic environment is responsible for activating a multitude of genes involved in angiogenesis,metastasis,invasiveness,pH changes,metabolic reprogramming,stem cell maintenance,resistance to radiation,and downstream regulation of the immune system.Treatment with P1 and P2 groups helped minimize the ACF count and restored HRV factors,weight disparity,pH levels,total acidity,and oxidative balance.Our findings emphasize the potential role of 1,2,4-triazine derivatives in suppressing hypoxia-induced colon carcinogenesis.

Nested Levels of Hybrid Cryptographical Technique for Secure Information Exchange

Data security is a very important part of data transmission over insecure channels connected through high-speed networks. Due to COVID-19, the use of data transmission over insecure channels has increased in an exponential manner. Hybrid cryptography provides a better solution than a single type of cryptographical technique. In this paper, nested levels of hybrid cryptographical techniques are investigated with the help of Deoxyribonucleic Acid (DNA) and Paillier cryptographical techniques. In the first level, information will be encrypted by DNA and at the second level, the ciphertext of DNA will be encrypted by Paillier cryptography. At the decryption time, firstly Paillier cryptography will be processed, and then DAN cryptography will be processed to get the original text. The proposed algorithm follows the concept of Last Encryption First Decryption (LEFD) at the time of decryption. The computed results are depicted in terms of tables and graphs.

A Systematic Review on the Internet of Medical Things:Techniques,Open Issues,and Future Directions

IoT usage in healthcare is one of the fastest growing domains all over the world which applies to every age group.Internet of Medical Things(IoMT)bridges the gap between the medical and IoT field where medical devices communicate with each other through a wireless communication network.Advancement in IoMT makes human lives easy and better.This paper provides a comprehensive detailed literature survey to investigate different IoMT-driven applications,methodologies,and techniques to ensure the sustainability of IoMT-driven systems.The limitations of existing IoMTframeworks are also analyzed concerning their applicability in real-time driven systems or applications.In addition to this,various issues(gaps),challenges,and needs in the context of such systems are highlighted.The purpose of this paper is to interpret a rigorous review concept related to IoMT and present significant contributions in the field across the research fraternity.Lastly,this paper discusses the opportunities and prospects of IoMT and discusses various open research problems.

Measuring the agricultural sustainability of India:An application of Pressure-State-Response(PSR)model

Analyzing agricultural sustainability is essential for designing and assessing rural development initiatives.However,accurately measuring agricultural sustainability is complicated since it involves so many different factors.This study provides a new suite of quantitative indicators for assessing agricultural sustainability at regional and district levels,involving environmental sustainability,social security,and economic security.Combining the PressureState-Response(PSR)model and indicator approach,this study creates a composite agricultural sustainability index for the 14 mainstream agro-climatic regions of India.The results of this study show that the Trans-Gengatic Plain Region(TGPR)ranks first in agricultural sustainability among India's 14 mainstream agro-climatic regions,while the Eastern Himalayan Region(EHR)ranks last.Higher livestock ownership,cropping intensity,per capita income,irrigation intensity,share of institutional credit,food grain productivity,crop diversification,awareness of minimum support price,knowledge sharing with fellow farmers,and young and working population,as well as better transportation facilities and membership of agricultural credit societies are influencing indicators responsible for higher agricultural sustainability in TGPR compared with EHR.Although,the scores of environmental sustainability indicators of EHR are quite good,its scores of social and economic security indicators are fairly low,putting it at the bottom of the rank of agricultural sustainability index among the 14 mainstream agroclimatic regions in India.This demonstrates the need of understanding agricultural sustainability in relation to social and economic dimensions.In a nation as diverse and complicated as India,it is the social structure that determines the health of the economy and environment.Last but not least,the sustainability assessment methodology may be used in a variety of India's agro-climatic regions.

Interpretive Structural Modeling Based Assessment and Optimization of Cloud with Internet of Things (CloudIoT) Issues Through Effective Scheduling

Integrated CloudIoT is an emergingfield of study that integrates the Cloud and the Internet of Things(IoT)to make machines smarter and deal with real-world objects in a distributed manner.It collects data from various devices and analyses it to increase efficiency and productivity.Because Cloud and IoT are complementary technologies with distinct areas of application,integrating them is difficult.This paper identifies various CloudIoT issues and analyzes them to make a relational model.The Interpretive Structural Modeling(ISM)approach establishes the interrelationship among the problems identified.The issues are categorised based on driving and dependent power,and a hierarchical model is presented.The ISM analysis shows that scheduling is an important aspect and has both(driving and dependence)power to improve the performance of the CloudIoT model.Therefore,existing CloudIoT job scheduling algorithms are ana-lysed,and a cloud-centric scheduling mechanism is proposed to execute IoT jobs on a suitable cloud.The cloud implementation using an open-source framework to simulate Cloud Computing(CloudSim),based on the job’s workload,is pre-sented.Simulation results of the proposed scheduling model indicate better per-formance in terms of Average Waiting Time(AWT)and makespan than existing cloud-based scheduling approaches.

Security Test Case Prioritization through Ant Colony Optimization Algorithm

Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems.One of the challenges in software security testing is test case prioritization,which aims to reduce redundancy in fault occurrences when executing test suites.By effectively applying test case prioritization,both the time and cost required for developing secure software can be reduced.This paper proposes a test case prioritization technique based on the Ant Colony Optimization(ACO)algorithm,a metaheuristic approach.The performance of the ACO-based technique is evaluated using the Average Percentage of Fault Detection(APFD)metric,comparing it with traditional techniques.It has been applied to a Mobile Payment Wallet application to validate the proposed approach.The results demonstrate that the proposed technique outperforms the traditional techniques in terms of the APFD metric.The ACO-based technique achieves an APFD of approximately 76%,two percent higher than the second-best optimal ordering technique.These findings suggest that metaheuristic-based prioritization techniques can effectively identify the best test cases,saving time and improving software security overall.

A Trailblazing Framework of Security Assessment for Traffic Data Management

Connected and autonomous vehicles are seeing their dawn at this moment.They provide numerous benefits to vehicle owners,manufacturers,vehicle service providers,insurance companies,etc.These vehicles generate a large amount of data,which makes privacy and security a major challenge to their success.The complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their predecessors.This could have a negative impact on how well-liked CAVs are with the general public,give them a poor name at this early stage of their development,put obstacles in the way of their adoption and expanded use,and complicate the economic models for their future operations.On the other hand,congestion is still a bottleneck for traffic management and planning.This research paper presents a blockchain-based framework that protects the privacy of vehicle owners and provides data security by storing vehicular data on the blockchain,which will be used further for congestion detection and mitigation.Numerous devices placed along the road are used to communicate with passing cars and collect their data.The collected data will be compiled periodically to find the average travel time of vehicles and traffic density on a particular road segment.Furthermore,this data will be stored in the memory pool,where other devices will also store their data.After a predetermined amount of time,the memory pool will be mined,and data will be uploaded to the blockchain in the form of blocks that will be used to store traffic statistics.The information is then used in two different ways.First,the blockchain’s final block will provide real-time traffic data,triggering an intelligent traffic signal system to reduce congestion.Secondly,the data stored on the blockchain will provide historical,statistical data that can facilitate the analysis of traffic conditions according to past behavior.

Evaluating Security of Big Data Through Fuzzy Based Decision-Making Technique

In recent years,it has been observed that the disclosure of information increases the risk of terrorism.Without restricting the accessibility of information,providing security is difficult.So,there is a demand for time tofill the gap between security and accessibility of information.In fact,security tools should be usable for improving the security as well as the accessibility of information.Though security and accessibility are not directly influenced,some of their factors are indirectly influenced by each other.Attributes play an important role in bridging the gap between security and accessibility.In this paper,we identify the key attributes of accessibility and security that impact directly and indirectly on each other,such as confidentiality,integrity,availability,and severity.The significance of every attribute on the basis of obtained weight is important for its effect on security during the big data security life cycle process.To calculate the proposed work,researchers utilised the Fuzzy Analytic Hierarchy Process(Fuzzy AHP).Thefindings show that the Fuzzy AHP is a very accurate mechanism for determining the best security solution in a real-time healthcare context.The study also looks at the rapidly evolving security technologies in healthcare that could help improve healthcare services and the future prospects in this area.

A Framework for Securing Saudi Arabian Hospital Industry: Vision-2030 Perspective

Recent transformation of Saudi Arabian healthcare sector into a reven-ue producing one has signaled several advancements in healthcare in the country.Transforming healthcare management into Smart hospital systems is one of them.Secure hospital management systems which are breach-proof only can be termed as effective smart hospital systems.Given the perspective of Saudi Vision-2030,many practitioners are trying to achieve a cost-effective hospital management sys-tem by using smart ideas.In this row,the proposed framework posits the main objectives for creating smart hospital management systems that can only be acknowledged by managing the security of healthcare data and medical practices.Further,the proposed framework will also be helpful in gaining satisfactory rev-enue from the healthcare sector by reducing the cost and time involved in mana-ging the smart hospital system.The framework is based on a hybrid approach of three key methods which include:employing the Internet of Medical Things(IoMT)and blockchain methodologies for maintaining the security and privacy of healthcare data and medical practices,and using big data analytics methodol-ogy for raising the funds and revenue by managing the bulk volume of healthcare data.Moreover,the framework will also be helpful for both the patients and the doctors,thus enabling the Kingdom of Saudi Arabia(KSA)to meet its goals of Vision-2030 by ensuring low cost,yet credible,healthcare services.

Photoluminescence behavior and visible light photocatalytic activity of ZnO,ZnO/ZnS and ZnO/ZnS/α-Fe_2O_3 nanocomposites

In order to achieve effective, economic, and easily achievable photocatalyst for the degradation of dye methyl orange（MeO）, ZnO, ZnO/ZnS and ZnO/ZnS/α-Fe2O3 nanocomposites were prepared by simple chemical synthetic route in the aqueous medium. Phase, crystallinity, surface structure and surface behavior of the synthesized materials were determined by X-ray diffraction（XRD） and Brunauer-Emmett-Teller analysis（BET） techniques. XRD study established formation of good crystalline ZnO, ZnO/ZnS and ZnO/ZnS/α-Fe2O3 nanomaterials. By using intensity of constituent peaks in the XRD pattern, the compositions of nanocomposites were determined. From the BET analysis, the prepared materials show mesoporous behavior, type Ⅳ curves along with H4 hysteresis. The ZnO/ZnS/α-Fe2O3 composite shows the largest surface area among three materials. From the UV-visible spectra, the band gap energy of the materials was determined. Photoluminescence spectra（PL） were used to determine the emission behavior and surface defects in the materials. In PL spectra, the intensity of UV peak of ZnO/ZnS is lowered than that of ZnO while in case of ZnO/ZnS/α-Fe2O3, the intensity further decreased. The visible emission spectra of ZnO/ZnS increased compared with ZnO while in ZnO/ZnS/α-Fe2O3 it is further increased compared with ZnO/ZnS. The as-synthesized materials were used as photocatalysts for the degradation of dye MeO. The photo-degradation data revealed that the ZnO/ZnS/α-Fe2O3 is the best photocatalyst among three specimens for the degradation of dye MeO. The decrease of intensity of UV emission peak and the increase of intensity of visible emission cause the decrease of recombination of electrons and holes which are ultimately responsible for the highest photocatalytic activity of ZnO/ZnS/α-Fe2O3.

Evaluating the Impact of Prediction Techniques: Software Reliability Perspective

Maintaining software reliability is the key idea for conducting quality research.This can be done by having less complex applications.While developers and other experts have made signicant efforts in this context,the level of reliability is not the same as it should be.Therefore,further research into the most detailed mechanisms for evaluating and increasing software reliability is essential.A signicant aspect of growing the degree of reliable applications is the quantitative assessment of reliability.There are multiple statistical as well as soft computing methods available in literature for predicting reliability of software.However,none of these mechanisms are useful for all kinds of failure datasets and applications.Hence nding the most optimal model for reliability prediction is an important concern.This paper suggests a novel method to substantially pick the best model of reliability prediction.This method is the combination of analytic hierarchy method(AHP),hesitant fuzzy(HF)sets and technique for order of preference by similarity to ideal solution(TOPSIS).In addition,using the different iterations of the process,procedural sensitivity was also performed to validate the ndings.The ndings of the software reliability prediction models prioritization will help the developers to estimate reliability prediction based on the software type.

Integrating Blockchain Technology into Healthcare Through an Intelligent Computing Technique

The blockchain technology plays a significant role in the present era of information technology.In the last few years,this technology has been used effectively in several domains.It has already made significant differences in human life,as well as is intended to have noticeable impact in many other domains in the forthcoming years.The rapid growth in blockchain technology has created numerous new possibilities for use,especially for healthcare applications.The digital healthcare services require highly effective security methodologies that can integrate data security with the availablemanagement strategies.To test and understand this goal of security management in Saudi Arabian perspective,the authors performed a numerical analysis and simulation through a multi criteria decision making approach in this study.The authors adopted the fuzzy Analytical Hierarchy Process(AHP)for evaluating the effectiveness and then applied the fuzzy Technique forOrder of Preference by Similarity to Ideal Solution(TOPSIS)technique to simulate the validation of results.For eliciting highly corroborative and conclusive results,the study referred to a real time project of diabetes patients’management application of Kingdom of Saudi Arabia(KSA).The results discussed in this paper are scientifically proven and validated through various analysis approaches.Hence the present study can be a credible basis for other similar endeavours being undertaken in the domain of blockchain research.

Estimating the Impact of COVID-19 Pandemic on the Research Community in the Kingdom of Saudi Arabia

Ever since its outbreak inWuhan,COVID-19 has cloaked the entireworld in a pall of despondency and uncertainty.The present study describes the exploratory analysis of all COVID cases in Saudi Arabia.Besides,the study has executed the forecastingmodel for predicting the possible number of COVID-19 cases in Saudi Arabia till a defined period.Towards this intent,the study analyzed different age groups of patients(child,adult,elderly)who were affected by COVID-19.The analysis was done city-wise and also included the number of recoveries recorded in different cities.Furthermore,the study also discusses the impact of COVID-19 on the economy.For conducting the stated analysis,the authors have created a list of factors that are known to cause the spread of COVID-19.As an effective countermeasure to contain the spread of Coronavirus in Saudi Arabia,this study also proposes to identify the most effective Computer Science technique that can be used by healthcare professionals.For this,the study employs the Fuzzy-Analytic Hierarchy Process integrated with the Technique for Order Performance by Similar to Ideal Solution(F.AHP.TOPSIS).After prioritizing the various Computer Science techniques,the ranking order that was obtained for the different techniques/tools to contain COVID-19 was:A4>A1>A2>A5>A3.Since the Blockchain technique obtained the highest priority,the study recommends that it must be used extensively as an efficacious and accurate means to combat COVID-19.

Usability Evaluation Through Fuzzy AHP-TOPSIS Approach: Security Requirement Perspective

Most of the security strategies today are primarily designed to provide security protection,rather than to solve one of the basic security issues related to adequate software product architecture.Several models,frameworks and methodologies have been introduced by the researchers for a secure and sustainable software development life cycle.Therefore it is important to assess the usability of the popular security requirements engineering(SRE)approaches.A significant factor in the management and handling of successful security requirements is the assessment of security requirements engineering method performance.This assessment will allow changes to the engineering process of security requirements.The consistency of security requirements depends heavily on the usability of security requirements engineering.Several SRE approaches are available for use and each approach takes into account several factors of usability but does not cover every element of usability.There seems to be no realistic implementation of such models because the concept of usability is not specific.This paper aims at specifying the different taxonomy of usability and design hierarchical usability model.The taxonomy takes into account the common quality assessment parameters that combine variables,attributes,and characteristics identified in different approaches used for security requirements engineering.The multiple-criteria decision-making(MCDM)model used in this paper for usability evaluation is called the fuzzy AHP-TOPSIS model which can conveniently be incorporated into the current approach of software engineering.Five significant usability criteria are identified and used to evaluate the six different alternatives.Such strategies are graded as per their expected values of usability.

Integrity Assessment of Medical Devices for Improving Hospital Services

The present study examines the various techniques being used to maintain the integrity of the medical devices,and develops a quantitative framework to list these in the sequence of priority.To achieve the intended objective,the study employs the combined procedure of Fuzzy Analytic Network Process(ANP)and Fuzzy Technical for Order Preference by Similarities to Ideal Solution(TOPSIS).We selected fuzzy based decision making techniques for assessing the integrity of medical devices.The suggested methodology was then used for classifying the suitable techniques used to evaluate the integrity of medical devices.Different techniques or the procedures of integrity assessment were ranked according to their satisfaction weights.The rating of the options determined the order of priority for the procedures.As per the findings of the study,among all the options,A1 was assessed to be the most likely option.This means that the integrity of medical devices of A2 is the highest amongst all the chosen alternatives.This analysis will be a corroborative guideline for manufacturers and developers to quantitatively test the integrity of medical devices in order to engineer efficacious devices.The evaluations undertaken with the assistance of the planned procedure are accurate and conclusive.Hence instead of conducting a manual valuation,this experimental study is a better and reliable option for assessing the integrity of the medical devices.

Hesitant Fuzzy-Sets Based Decision-Making Model for Security Risk Assessment

Security is an important component in the process of developing healthcare web applications.We need to ensure security maintenance;therefore the analysis of healthcare web application’s security risk is of utmost importance.Properties must be considered to minimise the security risk.Additionally,security risk management activities are revised,prepared,implemented,tracked,and regularly set up efficiently to design the security of healthcare web applications.Managing the security risk of a healthcare web application must be considered as the key component.Security is,in specific,seen as an add-on during the development process of healthcare web applications,but not as the key problem.Researchers must ensure that security is taken into account right from the earlier developmental stages of the healthcare web application.In this row,the authors of this study have used the hesitant fuzzy-based AHP-TOPSIS technique to estimate the risks of various healthcare web applications for improving security-durability.This approach would help to design and incorporate security features in healthcare web applications that would be able to battle threats on their own,and not depend solely on the external security of healthcare web applications.Furthermore,in terms of healthcare web application’s security-durability,the security risk variable is measured,and vice versa.Hence,the findings of our study will also be useful in improving the durability of several web applications in healthcare.

Impact Assessment of COVID-19 Pandemic Through Machine Learning Models

Ever since its outbreak in the Wuhan city of China,COVID-19 pandemic has engulfed more than 211 countries in the world,leaving a trail of unprecedented fatalities.Even more debilitating than the infection itself,were the restrictions like lockdowns and quarantine measures taken to contain the spread of Coronavirus.Such enforced alienation affected both the mental and social condition of people significantly.Social interactions and congregations are not only integral part of work life but also form the basis of human evolvement.However,COVID-19 brought all such communication to a grinding halt.Digital interactions have failed to enthuse the fervor that one enjoys in face-to-face meets.The pandemic has shoved the entire planet into an unstable state.The main focus and aim of the proposed study is to assess the impact of the pandemic on different aspects of the society in Saudi Arabia.To achieve this objective,the study analyzes two perspectives:the early approach,and the late approach of COVID-19 and the consequent effects on different aspects of the society.We used a Machine Learning based framework for the prediction of the impact of COVID-19 on the key aspects of society.Findings of this research study indicate that financial resources were the worst affected.Several countries are facing economic upheavals due to the pandemic and COVID-19 has had a considerable impact on the lives as well as the livelihoods of people.Yet the damage is not irretrievable and the world’s societies can emerge out of this setback through concerted efforts in all facets of life.