This section of the reSearch Comparises of A BRIEF Review of Past Papers / Research Regulating The Issue Consequency of Rising AQI and LUNG CANCER All. Kalaivani et al.7 stated the detection system made by the userDataSet of Computed Tomography (CT) Images WAS TAKEN Up for the Detection Purposes. FURTHER for the Classification of the LUNG Images DataSet the Images Are CLA CLA CLA CLA SSIFIED As Normal Or Malignant. A Densely Connected Convolution Neural Network Basically A Densenet Layer Made for the Classification and ImageDetection Purposes. Total 201 IMAGES Were Use and the Train Test Split of 85–15 WAS ENCOUNTERED at the Time of Model Configuration. Provides Better Feature Engineering Than Machine Learning on ITS Own. The Accuracy of 90.85% was Obtainedby the problemed model.

Kumar Et Al.8 Stated the Quality of Air Being Highly Dependent on the Number of Pollutants Afffaceing The Health of Humans. Vehicles Being the Major Cauise of Air Pollution in India The DataSet Comprises 23 Indian Cities of the Past SixYears. An Explrative Data Analysis was done after the feature scaling of the dataset to provate more visualInedAlIMental Results that can be concluded by the GIV. En Data. The DataSet has because resampled and further disifering techniques are using for Air Quality Prediction Model Such AS KNNGaussian Nasve Bayes, SVM RF and XGBOOST. The Best Accuracy PROVIDD WAS 91% by XGBOOST MODEL.

WEI Soh et al.9 proposed a deep Learning Approach to use deep Learning Methods to Forecast Air Quality for 2 Days. l networks with a composition of ann, cnn and lstm. FURTHER PROVIDIDIDIDIDIDINGOver Meteorology DataSet. The proposed model observes higher results in Specified Regions giving the best account account. D for Calculating the RMSE for Both the Training and Testing DataSet. The best performance was obServed in Taiwan and beijing.

Subramaniam et al.10 stated the effect of Air Pollution on Human Beings by Exploring The Application of Ai in Predicting Air PAPER AUTHOROSED Y Technology Such as Decision Tree, Machine Learning and Neural Networks in ForeCasting The Air Pollution and ITS EffectOn Human Health11. It is evidence that the Technologies Improve the Accuracy of Predicting Air Would Help In Reducts Was Ghich ​​was argued by the author. Several Different Approaches Such as Machine Learning Algorithms, Chemical Transport Models Which ALSOHelp in prediting in preditingUdabur Wealth Management. The author states the limitations of the current siture scope for prediting Air Polls the Paper. This p Aper Highlights An Important OverView of the Need of Ai Technologies and Their Potential in Prediction Air Polluting and why it's ImportantTo continue the reSearch in this particular fire.

Previous Researchars Discusted Solely About The Air Pollution Description The Levels of Aqi and the Gradual Increase in The Air Pollution All Over Indiauing Ting T HE POLITICAL MAPS of India Showcasing The Different Levels of Air Quality Index with Tables of aqi color levels allWith Respect to Range PROVIDED. The Data for Such Papers is Made Available at Iqair Website WHERE One CHECK for Worldwide Air Quality Index at any time. E Research Papers are further discussed in table 1.

The table 1 discusses about the dataset and algorithms userd by the reSearchers in the explained pain. E like review process in the pain.

SCHULZE ET AL.12 Stated the Effects of Air Pollution on Health and How Microfluidic Chips Is used to Fix TheSe EffectsKanpur Investment. ItRogen Dioxide and Oxone Who is Found in Air and their Harmful Effects on Health Such As Cardiovascular and RespiratoryDiseases was given by the author, He Further Discusses the Propms Involved with Measuring the Air Qich Includes The Need for Real-Time Data. The Author Highlights use of microfluidic chips which are a solution for fixing the quality of Air, they are basicallySmall Tools Which Control Fluids of Small Volume that Enables the Air Pollutant Measurement Concentrations in Real Team; TheRe Are Various KINDS of Microfluidi CHIPS Which Are Developed Over Time to Monitor the Quality of Air and Which Measure PARTICULATE MATER.The Fact that is a need to continue research so that the account of Microfluidic Chips can be improved and their application is expanded to a larger right. llutants.

Gupta et al.13 This Paper Stated that for Predicting the Presence of LUNG CANCER the USAGE of Machine Learning Algorithm is very Important. MS of Machine Learning Such as Support Vector Machines, DECISION TREES, RANDOM FORESTS Are Use in Prediction LUNGCANCER. The Authortes About the Conventional Ways of Diagnosing LUNG CANCER and How Important More Effective Methods Are Early Detection of LUNG CANCER. ET that is used by the author included clinical features, Images of CT Scan and DEMOGRAPHICS. The author highlights howImportant the use of feature selection is in import the performance of the machine learning models. The Paper is Concluded by Discussing The Limitations of Current Me Thods and the Future Directions of This Study. The Author Describes the Importation of Clinical Validation and the Need of Large andMore diverse dataset to enhance the performance of Machine Learning Algorithms.

Kumar Et Al.14 in this Paper Machine Learning Algorithms WERE USED Text DataSets for Predicting The Presence Which Included Logology , Naive Bayes and Decision Trees. The Author Describes How LUNG CANCER is Diagnosed and Treated using method and then givesAn Overview of DataSet used in this painting that is Pathology Reports and Text Data from Medical Records of the Patient. ARNINING ALGORTHMS FROM Text DataSets for Predicting LUNG CANCER WAS PRESENTD BY THE AUTHOR, He Also Highlight How Salient Feature SelectionIs in Improvision the Performance of LUNG CANCER. Er Performance and Importance of Clinical Validation and How Critical The Synergy BetWeen Medical Professionals and Computer Scientis isThen, then

Sumathi et al.15 has messaged Various Techniques of Air Quality PredictionHyderabad Investment. The Authors Explained About the Detailed Effect of Air Quality on the Health of P P p p EOPLE and the ENVIRONMENT. The DataSET USED in the Paper is Extracted from Various MonitoringsKolkata Stocks. Classification techniques are usedSuch as knn, decision tree and svm. The propsed approach predominantly discusses about the feature selection process to improVE the Results process by the. Algorithms. Also, The Ensember Methods and Deep Learning Approach Is used for options of the select model of the selectThe Conclusion that deep Learning Models are more reliable than the value, OVIDE Better and More Accurate Results.

Bhattacharya et al.16 Talks About the Air Quality Predictive Models for Aqi Prediction in New Delhi. Various Machine Learning Algorithms Used. The Data Use in The Paper was expected by the Different Monitorings and MeteorologyData for Air Quality. The Paper Majorly Focuses On the Importance of Air Quality Prediction for Environment and HealthCares of Delhi to Precis due to the Ajor Degradation of Air Quality in Past Few Years. The authors FURTHER MENTINED the Results and the Conclusion PROVIDED byThe Models used in the proposed approach that is decision tress, Random Forests and Artificial Neural Networks Also the user of hybrid model Timized Predictive Models. The Best Accuracy Provided was 93.4%.

Further The Brief Detailing of the PaPers is Provided in Table 2. This Table Provides a Briefs MenTioned in the LITERATUREREVIEW PROVIDID in TH is Paper.

Behera et al.17 Presented An Overall Report of the Load and Administration of LUNG CANCER INDIA. It Mainly Focuses on the Expanding Incidence as the Fatality R ATES and the Objects and Convenience for the Enhancement for the Medication of the Illness. The AspectOf Tobacco USAGE, UncleAnet Environment and the Genetic Aspects and the Progression Have Been Discussed by the Author. LOSURE of the Cancer and To CUT DOWN The USE of Tobacco and Pollution. The Need of Extra Extensive and Authentic Data On theLoad Handling and the Combined Passage for the Avoidance, Diagnosis and the Presibility for the Enhancement's Health and to decrease.

Dritsas Et Al.11 States the Prediction by Giving the Elaborat Study About the Execution of Discovering The LUNG CANCER BY PERFORME MACHINE Learning The analysis of the carncer risk by focusing the factors of public health and climate.Lifestyle and demural, Data Which Gives The Efficience by USING The Feature Selection Through Some of the Hybrid Models. SED for the Analysis are the decision tree, support vector Machine and the logistic regression.CERTAINTY of The Algorithms by Presenting The Effect of Feature Selection and Data Pre-PROCESSING WHICH Enhances The Model's Accuracy. KS and the Coming Guidelines of the Study. The Urgeency of Getting More Extensive Data Carrying Some Extra Features and the Concern ofThe Genetic Aspects Into The Prediction of LUNG CANCER to Achieve Stable Forecasting.

Mustafa Et Al.19 Examines the usability of some machine learning algorithms for the Classification and ForeCasting of the LUNG CANCER Into Various Stages. It Out the Relevance of the CARADICTION for Civil Health by Drawing The Etiology and the Danger Aspects.Constitutes of Clinical and Histopathology Information from Victims ReCogNISED for LUNG CANCER. Etermined by USIND the Hybrid Models and Some Extra Complicated Algorithms by Highlight The Need of Feature Selection as the Data Pre-PROCESSING WhichIncrements the Results. The Drawbacks, FURTHER Guidance and the Demand of DataSet Including Environmental and Genetic Aspects for the Roth Forec Asting has discussed by the author. It gives out the beneficial control. CHfor the timent of the patients.

NAGESWARAM et al .20 States the Importance of Techniques Such as Image Processing and Machine Learning in Predicting LUNGER. itHMS and Classification of the Disease Based on CT Images Into Different StageS State this Paper in Detail.The Need of LUNG CANCER PREDICTION With Main Focus on Etiology and Risk Factors of LUNG CANCER in PROVIDED BYE AUTHOR. Cludes CT Images of Patients Diagnosed with LUNG CANCER.Which Predict LUNG CANCER BASED on CT Images by Classifying Disease Into Various Stages. The Need of Feature Selection and Image PROCESSING IS DISCUSED In Thi s Paper Which Improves The Efficience of the Model. While Concluding the Paper the Author DiscussesScopes of the Study, He Also States that there was a need of more comprising datasetasetasetasetase included more risk factors of Lung CANCER.

Critical Facets of Technology's Effects on Health Are Covered in Two Different Research Papers.

In Kalaivani et al .'s Study, main focus is on healthcare. (CNNS) and (RNNS) May Be user D Categorization of LUNG CANCER.Improves Patient Outcomes, this is of Utmost Clinical Significance. The authors are likely to have performed data preliminary processing, INVOLVING IM Age Enhancement and Feature Extraction, They Woul Tackled the Class Imbalance PROBLEM COMMON in Medical DataTASETS SUCHNIQUES SUCH As Oversampling OR weighted LossFunctions. In Contract, The Research Carried Out by Subramania Et Al. Combines the DISCIPLINES of Environmental Science and the Health of the Public. Ative Researchs Into How Artificial Intelligence (AI) Is Utilised TO ESTIMATE AIR POLLUTION Levels and Assess How It AffectsPeople's health. The Approach Includes An In-deflene Examification of the Body of Research, Highlight Several AI TECHNIQUES. GHTS The CapaCity of ai to OFFER Actual Time Environmental Information and Its Implications for Accurate Choices Regarding Public Health.Two Studies Serve as Excellent Instances for the Different Ways that Technolog is using in ENVIRONMENTAL and Healthcare. WHILE Subramaniam et al .'s Arch Explores How Ai Could Assist in Reducing the Health Risks Association with Air Pollution, Kalaivani et al .'s workEmploys Deep Learning for LUNG CANCER Evaluation to Tackle A Key Medical Issue. ISSUES and Proved used the fields they work in.