Pub Date : 2024-07-31DOI: 10.22214/ijraset.2024.63600
Rishav Biswas
Abstract: Methanolic extract of Ocimum sanctum leaves were used as a reducing and stabilizing agent for the synthesis of copper nanoparticles (CuNPs). It is a cost-effective and eco-friendly process. On the treatment of Ocimum sanctum leaf extract with copper sulphate solution, stable CuNPs were formed. The formed CuNPs were characterized under UV-Vis spectrophotometer. The biologically synthesized copper nanoparticles show high antibacterial activity against opportunistic pathogen Staphylococcus aureus. The antimicrobial activity was determined by three assays with agar well diffusion, minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) and the values were compared to observe the antimicrobial efficacy of the CuNPs.
{"title":"Antimicrobial Activity of Biosynthesized Copper Nanoparticles Using Methanolic Extract of Ocimum Sanctum","authors":"Rishav Biswas","doi":"10.22214/ijraset.2024.63600","DOIUrl":"https://doi.org/10.22214/ijraset.2024.63600","url":null,"abstract":"Abstract: Methanolic extract of Ocimum sanctum leaves were used as a reducing and stabilizing agent for the synthesis of copper nanoparticles (CuNPs). It is a cost-effective and eco-friendly process. On the treatment of Ocimum sanctum leaf extract with copper sulphate solution, stable CuNPs were formed. The formed CuNPs were characterized under UV-Vis spectrophotometer. The biologically synthesized copper nanoparticles show high antibacterial activity against opportunistic pathogen Staphylococcus aureus. The antimicrobial activity was determined by three assays with agar well diffusion, minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) and the values were compared to observe the antimicrobial efficacy of the CuNPs.","PeriodicalId":13718,"journal":{"name":"International Journal for Research in Applied Science and Engineering Technology","volume":"41 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141795182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.22214/ijraset.2024.63684
Revathi M, Dr. N. A. Vasanthi
Abstract: For human survival, water is an essential and indispensable resource, and preserving its purity is paramount to people's health. Contaminated drinking water can lead to serious health problems, such as cholera, diarrhea, and other waterborne illnesses. Thus, maintaining clean and safe water becomes essential to advancing public health. Recent research indicates that water-related ailments claim the lives of a noteworthy 3,575,000 individuals annually. Thus, a reliable indicator of water potability could significantly lower the prevalence of these illnesses. Machine learning algorithms have emerged as highly effective instruments for precisely and promptly monitoring water resources by accurately forecasting the quality of the water. The Drinking Water dataset on Kaggle is the source of the water samples used in this study, and various algorithms are used to estimate water potability based on these properties. Nine different metrics make up this dataset: pH, hardness, solids, trihalomethanes, sulphates, chloramines, organic carbon, conductivity, and turbidity. We seek to ascertain the potability of drinking water by utilizing a variety of algorithms, including Random Forest, SVM, Decision Tree, and KNN. Among other notable results, the Random Forest algorithm outperforms conventional machine learning models, producing an astounding accuracy of 99.5%. It also performs well, producing an accuracy of 74%. As a result, this study has great potential to supply researchers, water management professionals, and policymakers with accurate data on water quality, increasing the efficacy of water potability monitoring
{"title":"Water Potability Prediction Using Machine Learning","authors":"Revathi M, Dr. N. A. Vasanthi","doi":"10.22214/ijraset.2024.63684","DOIUrl":"https://doi.org/10.22214/ijraset.2024.63684","url":null,"abstract":"Abstract: For human survival, water is an essential and indispensable resource, and preserving its purity is paramount to people's health. Contaminated drinking water can lead to serious health problems, such as cholera, diarrhea, and other waterborne illnesses. Thus, maintaining clean and safe water becomes essential to advancing public health. Recent research indicates that water-related ailments claim the lives of a noteworthy 3,575,000 individuals annually. Thus, a reliable indicator of water potability could significantly lower the prevalence of these illnesses. Machine learning algorithms have emerged as highly effective instruments for precisely and promptly monitoring water resources by accurately forecasting the quality of the water. The Drinking Water dataset on Kaggle is the source of the water samples used in this study, and various algorithms are used to estimate water potability based on these properties. Nine different metrics make up this dataset: pH, hardness, solids, trihalomethanes, sulphates, chloramines, organic carbon, conductivity, and turbidity. We seek to ascertain the potability of drinking water by utilizing a variety of algorithms, including Random Forest, SVM, Decision Tree, and KNN. Among other notable results, the Random Forest algorithm outperforms conventional machine learning models, producing an astounding accuracy of 99.5%. It also performs well, producing an accuracy of 74%. As a result, this study has great potential to supply researchers, water management professionals, and policymakers with accurate data on water quality, increasing the efficacy of water potability monitoring","PeriodicalId":13718,"journal":{"name":"International Journal for Research in Applied Science and Engineering Technology","volume":"32 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141795189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.22214/ijraset.2024.63709
Ranjith K
Abstract: The market for second-hand luxury cars in India is witnessing a significant surge, expected to grow at a rate of 16.30% from 2024 to 2032. This growth is fueled by increased car manufacturing, rising disposable incomes, and a shift in consumer preferences towards luxury brands. However, accurately determining the resale value of these vehicles presents a challenge due to various influencing factors. In this dynamic market, informed decision-making is crucial for luxury car buyers. Digital platforms have revolutionized access to real-time market data, helping both buyers and sellers stay updated on pricing trends. Our research explores the complexities of predicting prices for pre-owned luxury cars and introduces a predictive analytics framework using advanced machine learning algorithms. We collected and preprocessed a comprehensive dataset and conducted an in-depth exploratory data analysis. Various regression techniques, including Linear Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting, were employed to forecast prices. These models were evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) to identify the most accurate predictive model. This study offers a systematic solution for price prediction, enhancing the buying process for stakeholders in the second-hand luxury car market
{"title":"Prediction of Resale Value of Pre-Owned Luxury Cars in the Indian Market Employing Machine Learning Techniques","authors":"Ranjith K","doi":"10.22214/ijraset.2024.63709","DOIUrl":"https://doi.org/10.22214/ijraset.2024.63709","url":null,"abstract":"Abstract: The market for second-hand luxury cars in India is witnessing a significant surge, expected to grow at a rate of 16.30% from 2024 to 2032. This growth is fueled by increased car manufacturing, rising disposable incomes, and a shift in consumer preferences towards luxury brands. However, accurately determining the resale value of these vehicles presents a challenge due to various influencing factors. In this dynamic market, informed decision-making is crucial for luxury car buyers. Digital platforms have revolutionized access to real-time market data, helping both buyers and sellers stay updated on pricing trends. Our research explores the complexities of predicting prices for pre-owned luxury cars and introduces a predictive analytics framework using advanced machine learning algorithms. We collected and preprocessed a comprehensive dataset and conducted an in-depth exploratory data analysis. Various regression techniques, including Linear Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting, were employed to forecast prices. These models were evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) to identify the most accurate predictive model. This study offers a systematic solution for price prediction, enhancing the buying process for stakeholders in the second-hand luxury car market","PeriodicalId":13718,"journal":{"name":"International Journal for Research in Applied Science and Engineering Technology","volume":"50 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141795269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.22214/ijraset.2024.63613
Sakshi Kirar, D. Maru, Rakesh Patwa
Abstract: This study investigates the impact of international wind loading regulations on tall buildings by analyzing major codes from the United States (ASCE 7), Australia (AS/NZS 1170.2), Canada (NBC), and India (IS 875). The research reveals significant differences in the estimation of wind loads, attributed to variations in exposure categories, wind speed profiles, and calculation methodologies. Notably, the gust loading factor is commonly used across these standards. The parameters used to estimate wind loads by the international standards are also discussed. The findings underscore the necessity for global wind load limitations and emphasize the importance of considering local factors to ensure the structural safety and integrity of tall buildings under varying wind conditions.
{"title":"Wind-Induced Responses in Tall Buildings Using International Standards: A Review","authors":"Sakshi Kirar, D. Maru, Rakesh Patwa","doi":"10.22214/ijraset.2024.63613","DOIUrl":"https://doi.org/10.22214/ijraset.2024.63613","url":null,"abstract":"Abstract: This study investigates the impact of international wind loading regulations on tall buildings by analyzing major codes from the United States (ASCE 7), Australia (AS/NZS 1170.2), Canada (NBC), and India (IS 875). The research reveals significant differences in the estimation of wind loads, attributed to variations in exposure categories, wind speed profiles, and calculation methodologies. Notably, the gust loading factor is commonly used across these standards. The parameters used to estimate wind loads by the international standards are also discussed. The findings underscore the necessity for global wind load limitations and emphasize the importance of considering local factors to ensure the structural safety and integrity of tall buildings under varying wind conditions.","PeriodicalId":13718,"journal":{"name":"International Journal for Research in Applied Science and Engineering Technology","volume":"23 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141795355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.22214/ijraset.2024.63654
A. Yadav
Abstract: The increasing demand for energy and the need for sustainable solutions have spurred interest in harnessing waste heat from exhaust systems as a potential energy source. This research explores the viability of energy generation using exhaust heat, focusing on converting thermal energy into electrical power. By examining various thermoelectric materials and heat recovery technologies, we aim to develop an efficient system that captures and utilizes waste heat from industrial processes, automotive exhausts, and power plants. The study investigates the thermodynamic principles, material properties, and design considerations necessary for optimizing energy conversion efficiency. The potential environmental benefits, including reduced greenhouse gas emissions and enhanced energy efficiency, are also discussed. Through experimental analysis and modeling, this research seeks to provide a comprehensive understanding of the practical applications and challenges in implementing exhaust heat energy generation systems, ultimately contributing to the advancement of sustainable energy solutions
{"title":"Energy Generation from Exhaust Heat: Technologies and Innovations","authors":"A. Yadav","doi":"10.22214/ijraset.2024.63654","DOIUrl":"https://doi.org/10.22214/ijraset.2024.63654","url":null,"abstract":"Abstract: The increasing demand for energy and the need for sustainable solutions have spurred interest in harnessing waste heat from exhaust systems as a potential energy source. This research explores the viability of energy generation using exhaust heat, focusing on converting thermal energy into electrical power. By examining various thermoelectric materials and heat recovery technologies, we aim to develop an efficient system that captures and utilizes waste heat from industrial processes, automotive exhausts, and power plants. The study investigates the thermodynamic principles, material properties, and design considerations necessary for optimizing energy conversion efficiency. The potential environmental benefits, including reduced greenhouse gas emissions and enhanced energy efficiency, are also discussed. Through experimental analysis and modeling, this research seeks to provide a comprehensive understanding of the practical applications and challenges in implementing exhaust heat energy generation systems, ultimately contributing to the advancement of sustainable energy solutions","PeriodicalId":13718,"journal":{"name":"International Journal for Research in Applied Science and Engineering Technology","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141795373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.22214/ijraset.2024.63524
S. Peruvazhuthi
Abstract: In recent times, the novel Coronavirus disease (COVID-19) has emerged as one of the most infectious diseases, causing significant public health crises across over 200 nations worldwide. Given the challenges associated with the timeconsuming and error-prone nature of detecting COVID-19 through Reverse Transcription-Polymerase Chain Reaction (RTPCR), there is a growing reliance on alternative methods, such as examining chest X-ray (CXR) images. Viral pneumonia symptoms include a persistent cough with mucus, fever, chills, shortness of breath, and chest pain, especially during deep breaths or coughing. These symptoms often overlap significantly with those of other respiratory infections, including COVID-19. Accurately predicting COVID-19 severity and distinguishing it from viral pneumonia is crucial for effective patient management. Deep learning models offer promise in automating this process. The chest X-ray (CXR) images undergo preprocessing through Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve their quality. These enhanced images are fed into ResNet50 and EfficientNet-B0, both renowned deep learning models. Comparative evaluation demonstrates ResNet50 achieving an accuracy of 92.58%, whereas EfficientNet-B0 achieves a higher accuracy of 93.08%. This study underscores the efficacy of deep learning in COVID-19 prediction. The findings suggest EfficientNet-B0’s potential for improved diagnostic accuracy. This methodology presents a promising approach for automated, accurate COVID-19 severity prediction and differentiation from viral pneumonia, aiding timely medical interventions.
{"title":"Deep Learning-Based Prediction of COVID-19 and Viral Pneumonia from Chest X-Ray Images","authors":"S. Peruvazhuthi","doi":"10.22214/ijraset.2024.63524","DOIUrl":"https://doi.org/10.22214/ijraset.2024.63524","url":null,"abstract":"Abstract: In recent times, the novel Coronavirus disease (COVID-19) has emerged as one of the most infectious diseases, causing significant public health crises across over 200 nations worldwide. Given the challenges associated with the timeconsuming and error-prone nature of detecting COVID-19 through Reverse Transcription-Polymerase Chain Reaction (RTPCR), there is a growing reliance on alternative methods, such as examining chest X-ray (CXR) images. Viral pneumonia symptoms include a persistent cough with mucus, fever, chills, shortness of breath, and chest pain, especially during deep breaths or coughing. These symptoms often overlap significantly with those of other respiratory infections, including COVID-19. Accurately predicting COVID-19 severity and distinguishing it from viral pneumonia is crucial for effective patient management. Deep learning models offer promise in automating this process. The chest X-ray (CXR) images undergo preprocessing through Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve their quality. These enhanced images are fed into ResNet50 and EfficientNet-B0, both renowned deep learning models. Comparative evaluation demonstrates ResNet50 achieving an accuracy of 92.58%, whereas EfficientNet-B0 achieves a higher accuracy of 93.08%. This study underscores the efficacy of deep learning in COVID-19 prediction. The findings suggest EfficientNet-B0’s potential for improved diagnostic accuracy. This methodology presents a promising approach for automated, accurate COVID-19 severity prediction and differentiation from viral pneumonia, aiding timely medical interventions.","PeriodicalId":13718,"journal":{"name":"International Journal for Research in Applied Science and Engineering Technology","volume":"18 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141795428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.22214/ijraset.2024.63512
Abraham A, Vijay Vignesh P
Abstract: The Vaniyambadi Municipality case study is highlighted in this paper's analysis of municipal economics. The economy has a big impact on urban planning and is essential to the development and well-being of cities. The paper highlights the value of effective revenue collection and management by looking at the Vaniyambadi Municipality's organizational structure and revenue administration. There is also discussion of the challenges the municipality faces, such as its reliance on government grants and subsidies, growing expenses, and declining investment income. The article provides ways to address these issues, including increasing income, decreasing reliance on handouts, and developing a long-term financial plan. Examining revenue and expense accounts reveals concerning trends that highlight the need for financial sustainability. The article explores the organizational structure, historical context and population statistics for the Vaniyambadi Municipality. It discusses the several economic sectors primary, secondary, and tertiary and how they impact the local economy. The document's conclusion lists challenges with expenditure and revenue generation and underlines how urgently these problems must be resolved if Vaniyambadi Municipality is to continue to be sustainable in the long run.
{"title":"Analyzing Economy at Municipal level - A Case Study of Vaniyambadi Municipality","authors":"Abraham A, Vijay Vignesh P","doi":"10.22214/ijraset.2024.63512","DOIUrl":"https://doi.org/10.22214/ijraset.2024.63512","url":null,"abstract":"Abstract: The Vaniyambadi Municipality case study is highlighted in this paper's analysis of municipal economics. The economy has a big impact on urban planning and is essential to the development and well-being of cities. The paper highlights the value of effective revenue collection and management by looking at the Vaniyambadi Municipality's organizational structure and revenue administration. There is also discussion of the challenges the municipality faces, such as its reliance on government grants and subsidies, growing expenses, and declining investment income. The article provides ways to address these issues, including increasing income, decreasing reliance on handouts, and developing a long-term financial plan. Examining revenue and expense accounts reveals concerning trends that highlight the need for financial sustainability. The article explores the organizational structure, historical context and population statistics for the Vaniyambadi Municipality. It discusses the several economic sectors primary, secondary, and tertiary and how they impact the local economy. The document's conclusion lists challenges with expenditure and revenue generation and underlines how urgently these problems must be resolved if Vaniyambadi Municipality is to continue to be sustainable in the long run.","PeriodicalId":13718,"journal":{"name":"International Journal for Research in Applied Science and Engineering Technology","volume":"9 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141795879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.22214/ijraset.2024.62168
Sahil Shah
Abstract: Our overall well-being depends heavily on our mental health, which has received more attention in recent years. At the heart of this platform lies a sophisticated chatbot system, meticulously crafted to provide empathetic and responsive interactions with users.this abstract introduces a pioneering mental health website designed to offer comprehensive assistance to individuals seeking to improve their mental well-being. This chatbot serves as a virtual companion, offering a safe space for individuals to express their thoughts, feelings, and concerns without fear of judgment or stigma. Crucially, this website goes beyond mere conversation; it offers real-time solutions to address mental health challenges head-on. Drawing upon evidencebased practices and therapeutic techniques, the platform provides users with actionable strategies to manage stress, anxiety, depression, and other common mental health issues.
{"title":"Navigating Wellness: Chatbot-Powered Solutions for Mental Health","authors":"Sahil Shah","doi":"10.22214/ijraset.2024.62168","DOIUrl":"https://doi.org/10.22214/ijraset.2024.62168","url":null,"abstract":"Abstract: Our overall well-being depends heavily on our mental health, which has received more attention in recent years. At the heart of this platform lies a sophisticated chatbot system, meticulously crafted to provide empathetic and responsive interactions with users.this abstract introduces a pioneering mental health website designed to offer comprehensive assistance to individuals seeking to improve their mental well-being. This chatbot serves as a virtual companion, offering a safe space for individuals to express their thoughts, feelings, and concerns without fear of judgment or stigma. Crucially, this website goes beyond mere conversation; it offers real-time solutions to address mental health challenges head-on. Drawing upon evidencebased practices and therapeutic techniques, the platform provides users with actionable strategies to manage stress, anxiety, depression, and other common mental health issues.","PeriodicalId":13718,"journal":{"name":"International Journal for Research in Applied Science and Engineering Technology","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141795191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.22214/ijraset.2024.63566
K. S. Kumari
Abstract: The quality of indoor air is a critical determinant of health and well-being, particularly Given the considerable amount of time individuals invest indoors. Recognizing the pivotal role of air quality, this paper introduces a novel Home Air Quality Monitoring System (HAQMS) designed to provide real-time, accurate assessments of air quality within residential environments. The HAQMS integrates advanced sensors and IoT (Internet of Things) technologies to detect and quantify a wide range of air pollutants, including particulate matter (PM2.5 and PM10), volatile organic compounds (VOCs), carbon dioxide (CO2), carbon monoxide (CO), and ozone (O3).The system architecture is delineated into three primary components: the sensor array for pollutant detection, a data processing unit employing advanced algorithms for real-time data analysis, and a user interface for displaying air quality metrics and providing health recommendations. Utilizing machine learning techniques, the system not only reports currentair quality but also predicts future air quality levels based on historical data and trend analysis. This predictive feature is pivotal for proactive measures in maintaining indoor air quality.
{"title":"Home Air Quality Monitoring System","authors":"K. S. Kumari","doi":"10.22214/ijraset.2024.63566","DOIUrl":"https://doi.org/10.22214/ijraset.2024.63566","url":null,"abstract":"Abstract: The quality of indoor air is a critical determinant of health and well-being, particularly Given the considerable amount of time individuals invest indoors. Recognizing the pivotal role of air quality, this paper introduces a novel Home Air Quality Monitoring System (HAQMS) designed to provide real-time, accurate assessments of air quality within residential environments. The HAQMS integrates advanced sensors and IoT (Internet of Things) technologies to detect and quantify a wide range of air pollutants, including particulate matter (PM2.5 and PM10), volatile organic compounds (VOCs), carbon dioxide (CO2), carbon monoxide (CO), and ozone (O3).The system architecture is delineated into three primary components: the sensor array for pollutant detection, a data processing unit employing advanced algorithms for real-time data analysis, and a user interface for displaying air quality metrics and providing health recommendations. Utilizing machine learning techniques, the system not only reports currentair quality but also predicts future air quality levels based on historical data and trend analysis. This predictive feature is pivotal for proactive measures in maintaining indoor air quality.","PeriodicalId":13718,"journal":{"name":"International Journal for Research in Applied Science and Engineering Technology","volume":"25 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141795223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.22214/ijraset.2024.63548
K. A. Akintoye
Abstract: Intrusion detection systems (IDSs) are crucial for computer security, as they identify and counteract malicious activities within computer networks. Anomaly-based IDSs, specifically, use classification models trained on historical data to detect these harmful activities. This paper proposes an enhanced IDS based on 3-level training and testing of machine learning models, feature selection, resampling, and normalization using Decision Tree, Gaussian Naïve Bayes, K-Nearest Neighbours, Logistic Regression, Random Forest, and Support Vector Machine. In the first stage, the six models are trained and evaluated using the original datasets after pre-processing. In the second stage, the models are built and tested with a resampled version of the dataset using the Synthetic Minority Oversampling Technique (SMOTE). In the third stage, the models are trained and tested with a dataset that has been both resampled and normalized using the standard scaling method. We employ the feature importance technique using the random forest model to select the essential features from NSL-KDD and UNSW-NB15 datasets. The results of our study surpass previous related research, with the decision tree achieving an accuracy, precision, recall, and F1 score of 99.99% on the UNSW-NB15 dataset. Additionally, the decision tree recorded an accuracy of 99.98%, precision of 99.97%, recall of 99.97%, and F1 score of 99.99% on the NSL-KDD dataset.
{"title":"Network Intrusion Detection and Classification System: A Supervised Machine Learning Approach","authors":"K. A. Akintoye","doi":"10.22214/ijraset.2024.63548","DOIUrl":"https://doi.org/10.22214/ijraset.2024.63548","url":null,"abstract":"Abstract: Intrusion detection systems (IDSs) are crucial for computer security, as they identify and counteract malicious activities within computer networks. Anomaly-based IDSs, specifically, use classification models trained on historical data to detect these harmful activities. This paper proposes an enhanced IDS based on 3-level training and testing of machine learning models, feature selection, resampling, and normalization using Decision Tree, Gaussian Naïve Bayes, K-Nearest Neighbours, Logistic Regression, Random Forest, and Support Vector Machine. In the first stage, the six models are trained and evaluated using the original datasets after pre-processing. In the second stage, the models are built and tested with a resampled version of the dataset using the Synthetic Minority Oversampling Technique (SMOTE). In the third stage, the models are trained and tested with a dataset that has been both resampled and normalized using the standard scaling method. We employ the feature importance technique using the random forest model to select the essential features from NSL-KDD and UNSW-NB15 datasets. The results of our study surpass previous related research, with the decision tree achieving an accuracy, precision, recall, and F1 score of 99.99% on the UNSW-NB15 dataset. Additionally, the decision tree recorded an accuracy of 99.98%, precision of 99.97%, recall of 99.97%, and F1 score of 99.99% on the NSL-KDD dataset.","PeriodicalId":13718,"journal":{"name":"International Journal for Research in Applied Science and Engineering Technology","volume":"50 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141795271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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