Dr. S. M. Kulkarni, S. S. Pawar, A. A. Dekhane, S. L. Suryawanshi
Image classification, especially in scenarios with limited data, presents significant challenges. Few shot learning (FSL) aims to address these challenges by training models that can generalize from a few examples. This paper explores the integration of prototypical networks with ResNet-18 for feature extraction to enhance image classification accuracy. Prototypical networks are designed to create a prototype representation for each class, which can then be used to classify new examples based on their distance to these prototypes. By leveraging ResNet-18's powerful feature extraction capabilities, we aim to improve the quality of these prototypes, thereby enhancing classification performance.We propose various methods for accuracy enhancement and optimization, including hyperparameter tuning, regularization techniques, and advanced methods like attention mechanisms and metric learning. Hyperparameter tuning involves adjusting the model's parameters to find the optimal settings that yield the best performance. Regularization techniques, such as dropout and weight decay, help prevent overfitting and improve the model's generalization capabilities. Advanced methods like attention mechanisms can focus on the most relevant parts of the image, while metric learning aims to learn a distance metric that better reflects the similarities between images.Our experiments on datasets like Mini-ImageNet and Omniglot demonstrate significant improvements in classification performance. These datasets are commonly used benchmarks in the few-shot learning community, allowing us to compare our results with existing methods. The integration of prototypical networks with ResNet-18, along with the proposed optimization techniques, provides a robust approach for tackling the challenges of image classification in few-shot learning scenarios. Key Words: Few-shot learning, ResNet-18, Prototypical Networks.
{"title":"Enhancing Image Classification Using Few-Shot Learning Prototypical Networks with ResNet-18: Detection, Accuracy Enhancement, and Optimization","authors":"Dr. S. M. Kulkarni, S. S. Pawar, A. A. Dekhane, S. L. Suryawanshi","doi":"10.55041/ijsrem36755","DOIUrl":"https://doi.org/10.55041/ijsrem36755","url":null,"abstract":"Image classification, especially in scenarios with limited data, presents significant challenges. Few shot learning (FSL) aims to address these challenges by training models that can generalize from a few examples. This paper explores the integration of prototypical networks with ResNet-18 for feature extraction to enhance image classification accuracy. Prototypical networks are designed to create a prototype representation for each class, which can then be used to classify new examples based on their distance to these prototypes. By leveraging ResNet-18's powerful feature extraction capabilities, we aim to improve the quality of these prototypes, thereby enhancing classification performance.We propose various methods for accuracy enhancement and optimization, including hyperparameter tuning, regularization techniques, and advanced methods like attention mechanisms and metric learning. Hyperparameter tuning involves adjusting the model's parameters to find the optimal settings that yield the best performance. Regularization techniques, such as dropout and weight decay, help prevent overfitting and improve the model's generalization capabilities. Advanced methods like attention mechanisms can focus on the most relevant parts of the image, while metric learning aims to learn a distance metric that better reflects the similarities between images.Our experiments on datasets like Mini-ImageNet and Omniglot demonstrate significant improvements in classification performance. These datasets are commonly used benchmarks in the few-shot learning community, allowing us to compare our results with existing methods. The integration of prototypical networks with ResNet-18, along with the proposed optimization techniques, provides a robust approach for tackling the challenges of image classification in few-shot learning scenarios. Key Words: Few-shot learning, ResNet-18, Prototypical Networks.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"47 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809842","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}
The financial markets have been significantly influenced by Covid19. Investors have reallocated their portfolios as a result of changing expectations for risk and return. In both academia and industry, building a portfolio via wise stock selection has been seenas a problem. The stock market's inherent uncertainties are to blame for this. Stock selection in a portfolio is impacted by anticipated price movement. The predictability of stock price changes has been disputed for a very long time, however. The random walk hypothesis (Fama, 1995) states that since stock price changes are unpredictable and lack memory, the past cannot foretell the future. Therefore, if the market is efficient, the stock price at the moment represents all the information. Since insider trading is required, it is impossible to outperform the market and is compatible with EMH. Therefore, the quest for effective forecasting techniques does not lead to consistent, long-term trendsthat can be predicted. According to the findings, investors have begun redistributing their portfolios across other equities in response to the current financial crisis related to COVID-19. But not all investors experience the same situation when switching from risky to risk- free investments.
{"title":"PRE - POST COVID 19 STOCK ANALYSIS OF ONGC","authors":"Vikram Kumar, Dr. Somya Vatsnayan","doi":"10.55041/ijsrem36772","DOIUrl":"https://doi.org/10.55041/ijsrem36772","url":null,"abstract":"The financial markets have been significantly influenced by Covid19. Investors have reallocated their portfolios as a result of changing expectations for risk and return. In both academia and industry, building a portfolio via wise stock selection has been seenas a problem. The stock market's inherent uncertainties are to blame for this. Stock selection in a portfolio is impacted by anticipated price movement. The predictability of stock price changes has been disputed for a very long time, however. The random walk hypothesis (Fama, 1995) states that since stock price changes are unpredictable and lack memory, the past cannot foretell the future. Therefore, if the market is efficient, the stock price at the moment represents all the information. Since insider trading is required, it is impossible to outperform the market and is compatible with EMH. Therefore, the quest for effective forecasting techniques does not lead to consistent, long-term trendsthat can be predicted. According to the findings, investors have begun redistributing their portfolios across other equities in response to the current financial crisis related to COVID-19. But not all investors experience the same situation when switching from risky to risk- free investments.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"4 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806448","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}
Deploying an AI (Artificial Intelligence) model in the data center initiates more responsibilities to the backend services such as Monitoring. It is required to monitor the performance of AI systems regularly to ensure that they meet the requirements and will not encounter any system performance issues. This whitepaper focuses on the importance of monitoring AI systems, the monitoring model, how to measure the performance of the system hardware resources such as CPU, Memory, disk and GPU, and tools to be used to monitor the system resources. Organisations can take necessary proactive maintenance actions before an incident is caused due to performance bottlenecks in the AI systems, proving the importance of monitoring the AI system. The goal of continuous monitoring of AI systems is to ensure the effective operation of AI systems throughout their lifecycle to meet several objectives such as performance, anomaly detection, security monitoring, data compliance and continuous improvements. Performance measurement of critical resources such as GPU, Memory and Storage by using suitable tools and configuring the alerts when the thresholds are reached on the identified resource threads. These measurements will be utilized to strengthen the AI system that will be stable for any performance bottlenecks.
{"title":"AI Hardware Resource Monitoring in the Data Center Environment","authors":"Nanduri Vijaya Saradhi","doi":"10.55041/ijsrem36782","DOIUrl":"https://doi.org/10.55041/ijsrem36782","url":null,"abstract":"Deploying an AI (Artificial Intelligence) model in the data center initiates more responsibilities to the backend services such as Monitoring. It is required to monitor the performance of AI systems regularly to ensure that they meet the requirements and will not encounter any system performance issues. This whitepaper focuses on the importance of monitoring AI systems, the monitoring model, how to measure the performance of the system hardware resources such as CPU, Memory, disk and GPU, and tools to be used to monitor the system resources. Organisations can take necessary proactive maintenance actions before an incident is caused due to performance bottlenecks in the AI systems, proving the importance of monitoring the AI system. The goal of continuous monitoring of AI systems is to ensure the effective operation of AI systems throughout their lifecycle to meet several objectives such as performance, anomaly detection, security monitoring, data compliance and continuous improvements. Performance measurement of critical resources such as GPU, Memory and Storage by using suitable tools and configuring the alerts when the thresholds are reached on the identified resource threads. These measurements will be utilized to strengthen the AI system that will be stable for any performance bottlenecks.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809348","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}
Saravana Kumar R, John Rohith J S, Sabarivasan S M, Mangalapriya S
This abstract introduces a novel approach to human-computer interaction through the development of a hybrid virtual mouse that seamlessly integrates eye tracking and hand tracking technologies. By combining the precision of eye gaze with the versatility of hand gestures, the hybrid system aims to redefine how users navigate digital interfaces. The creation of hybrid interaction techniques that provide users with flexible control options. The hybrid virtual mouse represents a promising step toward more intuitive and inclusive human-computer interaction, bridging the gap between physical limitations and digital exploration. Keywords: Computer Vision, human-computer Interface, Open CV, Image Processing.
{"title":"Human-Computer Interaction Through Digital Virtual Navigation System","authors":"Saravana Kumar R, John Rohith J S, Sabarivasan S M, Mangalapriya S","doi":"10.55041/ijsrem36729","DOIUrl":"https://doi.org/10.55041/ijsrem36729","url":null,"abstract":"This abstract introduces a novel approach to human-computer interaction through the development of a hybrid virtual mouse that seamlessly integrates eye tracking and hand tracking technologies. By combining the precision of eye gaze with the versatility of hand gestures, the hybrid system aims to redefine how users navigate digital interfaces. The creation of hybrid interaction techniques that provide users with flexible control options. The hybrid virtual mouse represents a promising step toward more intuitive and inclusive human-computer interaction, bridging the gap between physical limitations and digital exploration. Keywords: Computer Vision, human-computer Interface, Open CV, Image Processing.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"5 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141808819","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}
Businesses need to identify and segment their consumer base in order to effectively customize their strategies and improve customer satisfaction in the highly competitive market landscape of today. The goal of this study is to employ machine learning techniques to create a strong consumer segmentation model that will classify customers according to their demographics, behaviors, and purchase histories. Through the use of multiple clustering methods, including K-means, DBSCAN, and Hierarchical Clustering, the model seeks to find unique customer segments with shared attributes. To accomplish optimal segmentation, the segmentation process entails three steps: feature selection to identify the most significant features, model training, and data preprocessing to manage missing values and outliers. In-depth segment analysis is also included in the report to offer practical insights for better client retention tactics, tailored recommendations, and focused marketing efforts. The results of the study illustrate how machine learning may be used to find hidden patterns in consumer data, giving organizations the ability to make data- driven decisions. Organizations may improve their marketing efforts, allocate resources more efficiently, and eventually increase customer engagement and profitability by putting this customer segmentation strategy into practice. Key Words: K-Means Clustering, Hierarchical Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Cluster Plotting, Heatmaps, customer relationship management (CRM) system.
{"title":"CUSTOMER SEGMENTATION USING MACHINE LEARNING","authors":"Kiran D, A. C","doi":"10.55041/ijsrem36658","DOIUrl":"https://doi.org/10.55041/ijsrem36658","url":null,"abstract":"Businesses need to identify and segment their consumer base in order to effectively customize their strategies and improve customer satisfaction in the highly competitive market landscape of today. The goal of this study is to employ machine learning techniques to create a strong consumer segmentation model that will classify customers according to their demographics, behaviors, and purchase histories. Through the use of multiple clustering methods, including K-means, DBSCAN, and Hierarchical Clustering, the model seeks to find unique customer segments with shared attributes. To accomplish optimal segmentation, the segmentation process entails three steps: feature selection to identify the most significant features, model training, and data preprocessing to manage missing values and outliers. In-depth segment analysis is also included in the report to offer practical insights for better client retention tactics, tailored recommendations, and focused marketing efforts. The results of the study illustrate how machine learning may be used to find hidden patterns in consumer data, giving organizations the ability to make data- driven decisions. Organizations may improve their marketing efforts, allocate resources more efficiently, and eventually increase customer engagement and profitability by putting this customer segmentation strategy into practice. Key Words: K-Means Clustering, Hierarchical Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Cluster Plotting, Heatmaps, customer relationship management (CRM) system.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"29 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817038","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}
Srashti Shukla, Dr. Yashpal Singh, Dr. Meghana Mishra
Effective intravenous (IV) infusion management and monitoring are essential in today's healthcare environment to guarantee patient safety and improve the standard of care. In order to overcome the drawbacks of conventional IV drip monitoring techniques, this study describes the creation of a contactless, Internet of Things-driven infusion monitoring and management system. With the use of cutting-edge sensor technology and Internet of Things connection, the suggested system combines real-time IV drip liquid level monitoring and control, delivering precise and ongoing updates. Healthcare practitioners may now monitor and analyse patient data remotely thanks to the gathered data's constant updating and transmission to the Think Speak cloud platform. With its combination of real- time data collecting, cloud-based analytics, and emergency alert features, the proposed contactless IoT-driven infusion monitoring and management system represents a substantial improvement in healthcare technology. Using the power of IoT and cloud computing, this invention seeks to enhance overall patient outcomes, optimize IV treatment administration, and decrease human monitoring efforts. To increase patient safety, the system has an emergency alarm mechanism based on GSM modules. This feature ensures that caregivers are notified promptly of any irregularities or critical IV drip levels, allowing for prompt treatment. By providing real-time signals, the device lessens the risks related to delayed reactions to IV fluid failure or depletion. Formulating a real-time model for IV drip liquid level monitoring is the main goal of the system. By employing non-invasive sensors to identify and record the drip rate and fluid levels, this gadget assures precise tracking without having physical contact with the IV apparatus. Keywords: Internet of Things, ESP32 microcontroller, Biomedical, GSM, Relay, LCD Display.
{"title":"Review on IoT-Driven Infusion Monitoring & Management System","authors":"Srashti Shukla, Dr. Yashpal Singh, Dr. Meghana Mishra","doi":"10.55041/ijsrem36700","DOIUrl":"https://doi.org/10.55041/ijsrem36700","url":null,"abstract":"Effective intravenous (IV) infusion management and monitoring are essential in today's healthcare environment to guarantee patient safety and improve the standard of care. In order to overcome the drawbacks of conventional IV drip monitoring techniques, this study describes the creation of a contactless, Internet of Things-driven infusion monitoring and management system. With the use of cutting-edge sensor technology and Internet of Things connection, the suggested system combines real-time IV drip liquid level monitoring and control, delivering precise and ongoing updates. Healthcare practitioners may now monitor and analyse patient data remotely thanks to the gathered data's constant updating and transmission to the Think Speak cloud platform. With its combination of real- time data collecting, cloud-based analytics, and emergency alert features, the proposed contactless IoT-driven infusion monitoring and management system represents a substantial improvement in healthcare technology. Using the power of IoT and cloud computing, this invention seeks to enhance overall patient outcomes, optimize IV treatment administration, and decrease human monitoring efforts. To increase patient safety, the system has an emergency alarm mechanism based on GSM modules. This feature ensures that caregivers are notified promptly of any irregularities or critical IV drip levels, allowing for prompt treatment. By providing real-time signals, the device lessens the risks related to delayed reactions to IV fluid failure or depletion. Formulating a real-time model for IV drip liquid level monitoring is the main goal of the system. By employing non-invasive sensors to identify and record the drip rate and fluid levels, this gadget assures precise tracking without having physical contact with the IV apparatus. Keywords: Internet of Things, ESP32 microcontroller, Biomedical, GSM, Relay, LCD Display.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"53 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141814814","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}
Word Sense Disambiguation (WSD) is a fundamental task in natural language processing (NLP) that focuses on determining the precise meaning of a word by analyzing its contextual usage.This paper presents a comprehensive analysis of various WSD techniques applied to the Punjabi language, including supervised, unsupervised, and knowledge-based methods. We compare the accuracy, performance, benefits, drawbacks, and resource requirements of these techniques.The study aims to provide a detailed overview of the state of WSD for Punjabi, with visual representations such as tables and graphs to illustrate comparative performance. Key Words: Word Sense Disambiguation, Punjabi Language, Natural Language Processing, Supervised Learning, Unsupervised Learning, Knowledge-Based Approach
{"title":"Evaluating Word Sense Disambiguation Techniques for Punjabi Language: A Comparative Analysis","authors":"Gursewak Singh","doi":"10.55041/ijsrem36699","DOIUrl":"https://doi.org/10.55041/ijsrem36699","url":null,"abstract":"Word Sense Disambiguation (WSD) is a fundamental task in natural language processing (NLP) that focuses on determining the precise meaning of a word by analyzing its contextual usage.This paper presents a comprehensive analysis of various WSD techniques applied to the Punjabi language, including supervised, unsupervised, and knowledge-based methods. We compare the accuracy, performance, benefits, drawbacks, and resource requirements of these techniques.The study aims to provide a detailed overview of the state of WSD for Punjabi, with visual representations such as tables and graphs to illustrate comparative performance. Key Words: Word Sense Disambiguation, Punjabi Language, Natural Language Processing, Supervised Learning, Unsupervised Learning, Knowledge-Based Approach","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"16 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141815648","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}
The quality of water is a critical parameter that affects human health, aquatic ecosystems, and environmental sustainability. The prediction of water quality using machine learning techniques has emerged as a promising solution for early detection and management of water pollution. This project focuses on developing a predictive model that leverages historical water quality data to forecast future water quality indices. Various machine learning algorithms, including regression and classification techniques, will be employed to analyze parameters such as pH, turbidity, dissolved oxygen, and contaminant levels. By training the model on a comprehensive dataset, the system aims to provide accurate and timely predictions, enabling proactive measures to be taken to ensure safe water supplies. The implementation of this model can significantly aid regulatory bodies and water management authorities in monitoring and maintaining water quality standards, ultimately contributing to public health and environmental conservation.
{"title":"WATER QUALITY PREDICTION USING MACHINE LEARNING TECHNIQUE","authors":"Er. P Nagalakshmi, Dr.P.Ganesh Kumar","doi":"10.55041/ijsrem36721","DOIUrl":"https://doi.org/10.55041/ijsrem36721","url":null,"abstract":"The quality of water is a critical parameter that affects human health, aquatic ecosystems, and environmental sustainability. The prediction of water quality using machine learning techniques has emerged as a promising solution for early detection and management of water pollution. This project focuses on developing a predictive model that leverages historical water quality data to forecast future water quality indices. Various machine learning algorithms, including regression and classification techniques, will be employed to analyze parameters such as pH, turbidity, dissolved oxygen, and contaminant levels. By training the model on a comprehensive dataset, the system aims to provide accurate and timely predictions, enabling proactive measures to be taken to ensure safe water supplies. The implementation of this model can significantly aid regulatory bodies and water management authorities in monitoring and maintaining water quality standards, ultimately contributing to public health and environmental conservation.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"69 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817697","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}
Heart disease is a major issue that has become increasingly prevalent. According to current statistics, heart disease claims the life of one person every minute. In the last several years, one of the hardest problems facing the medical field is predicting heart disease. Reducing the death rate can be achieved with early detection of cardiac disease. Machine learning is the most effective approach to forecasting heart disease. This paper aims to create a lightweight, straightforward solution to detecting cardiac disease using machine learning. Machine learning can aid in heart disease prediction. This study analyzes several machine learning algorithms and performance indicators. This study compares cardiac disease detection methods using a publicly available dataset from the UCI machine learning repository. There are other datasets accessible, including the Switzerland and Cleveland databases. Here the dataset contains 303 patient records and 18 characteristics. The analysis shows that out of six machine learning algorithms, the Random Forest algorithm gives the best result with 94.50%. Keywords- cardiac disease detection, datasets, heart disease prediction, Machine Learning, Random Forest algorithm.
{"title":"Heart Disease Prediction Using Machine Learning Algorithms","authors":"Mahammad Sahil Khan, Asst.Prof. Archana Panda","doi":"10.55041/ijsrem36570","DOIUrl":"https://doi.org/10.55041/ijsrem36570","url":null,"abstract":"Heart disease is a major issue that has become increasingly prevalent. According to current statistics, heart disease claims the life of one person every minute. In the last several years, one of the hardest problems facing the medical field is predicting heart disease. Reducing the death rate can be achieved with early detection of cardiac disease. Machine learning is the most effective approach to forecasting heart disease. This paper aims to create a lightweight, straightforward solution to detecting cardiac disease using machine learning. Machine learning can aid in heart disease prediction. This study analyzes several machine learning algorithms and performance indicators. This study compares cardiac disease detection methods using a publicly available dataset from the UCI machine learning repository. There are other datasets accessible, including the Switzerland and Cleveland databases. Here the dataset contains 303 patient records and 18 characteristics. The analysis shows that out of six machine learning algorithms, the Random Forest algorithm gives the best result with 94.50%. Keywords- cardiac disease detection, datasets, heart disease prediction, Machine Learning, Random Forest algorithm.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"24 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141816442","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}
Psoriasis, a complex inflammatory skin disorder, is characterized by rapid skin cell proliferation resulting in thick, red patches covered with silvery scales. This review explores the multidimensional approach to managing psoriasis, which encompasses current therapies such as systemic medications, topical treatments, and phototherapy, all known to have potential side effects. Recognizing the associated increased risk of comorbidities like psoriatic arthritis, anxiety, and cardiovascular diseases among others, this paper also delves into the promising role of herbal medicines which are gaining popularity due to their accessibility, cost-effectiveness, and potential efficacy. Additionally, it highlights the emerging advancements in novel drug delivery systems including liposomes, nanostructured lipid carriers, and microneedles, aimed at enhancing treatment efficacy through improved drug targeting and reduced side effects. This comprehensive review seeks to provide valuable insights for the development of safer and more effective therapeutic strategies, offering a beacon of hope for those afflicted by this chronic condition and guiding future research in the field. Keywords: Psoriasis, novel drug delivery systems, liposomes, nanostructured lipid carriers, microneedles, herbal medicine, lifestyle modifications, biologic agents
{"title":"Innovative Approaches in Psoriasis Management: Integration of Novel Drug Delivery Systems, Herbal Medicine, and Lifestyle Modifications","authors":"Krunal Detholia","doi":"10.55041/ijsrem36650","DOIUrl":"https://doi.org/10.55041/ijsrem36650","url":null,"abstract":"Psoriasis, a complex inflammatory skin disorder, is characterized by rapid skin cell proliferation resulting in thick, red patches covered with silvery scales. This review explores the multidimensional approach to managing psoriasis, which encompasses current therapies such as systemic medications, topical treatments, and phototherapy, all known to have potential side effects. Recognizing the associated increased risk of comorbidities like psoriatic arthritis, anxiety, and cardiovascular diseases among others, this paper also delves into the promising role of herbal medicines which are gaining popularity due to their accessibility, cost-effectiveness, and potential efficacy. Additionally, it highlights the emerging advancements in novel drug delivery systems including liposomes, nanostructured lipid carriers, and microneedles, aimed at enhancing treatment efficacy through improved drug targeting and reduced side effects. This comprehensive review seeks to provide valuable insights for the development of safer and more effective therapeutic strategies, offering a beacon of hope for those afflicted by this chronic condition and guiding future research in the field. Keywords: Psoriasis, novel drug delivery systems, liposomes, nanostructured lipid carriers, microneedles, herbal medicine, lifestyle modifications, biologic agents","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"35 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141816689","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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