This study looks at how Instagram users' relationships are affected and whether they thinktheir relationships are improving or deteriorating. In today's globalized society, social networking services (SNS) greatly influence how people interact and form connections. Instagram is a tool that couples use to strengthen their romantic relationships and reduce uncertainty when they are first dating. On the other hand, overusing the app can result in problems like jealousy, profile monitoring, poor communication, and time wastage. Feelings of outside influence can give rise to jealousy in relationships, which can cause bad emotions and a decrease in the enjoyment of the partnership. Knapp's relational stage model sheds light on how relationships develop and end. Sustaining healthy connections requires concentrating on good interactions in virtual spaces. The majority of young peopleusing Instagram are between the ages of 18 and 29, reflecting a shift in the demographic. Users are in total control of the online personas they create, and this might affect their romantic relationships. In October 2023, research was carried out to investigate the use of Instagram in different relationship stages. The results indicated that jealousy was asignificant predictor of daily Instagram usage, the importance of promoting one's relationship on the platform, and the daily duration of Instagram usage. Keywords: Social Media, Online and Offline, Communication, Emotion, Insecurity.
{"title":"Navigating Unrealistic Expectations of Relationships on Instagram","authors":"Swaleha Khanam, Dr. Tasha Singh Parihar","doi":"10.55041/ijsrem36695","DOIUrl":"https://doi.org/10.55041/ijsrem36695","url":null,"abstract":"This study looks at how Instagram users' relationships are affected and whether they thinktheir relationships are improving or deteriorating. In today's globalized society, social networking services (SNS) greatly influence how people interact and form connections. Instagram is a tool that couples use to strengthen their romantic relationships and reduce uncertainty when they are first dating. On the other hand, overusing the app can result in problems like jealousy, profile monitoring, poor communication, and time wastage. Feelings of outside influence can give rise to jealousy in relationships, which can cause bad emotions and a decrease in the enjoyment of the partnership. Knapp's relational stage model sheds light on how relationships develop and end. Sustaining healthy connections requires concentrating on good interactions in virtual spaces. The majority of young peopleusing Instagram are between the ages of 18 and 29, reflecting a shift in the demographic. Users are in total control of the online personas they create, and this might affect their romantic relationships. In October 2023, research was carried out to investigate the use of Instagram in different relationship stages. The results indicated that jealousy was asignificant predictor of daily Instagram usage, the importance of promoting one's relationship on the platform, and the daily duration of Instagram usage. Keywords: Social Media, Online and Offline, Communication, Emotion, Insecurity.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"70 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817590","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}
Abstract—Thanks to developments in Internet of Things (IoT) technology, safety and security alerting in smart homes has become a fundamental component of contemporary living. Smart houses are outfitted with an array of networked gadgets, in- cluding cameras, sensors, locks, and alarms, which collaborate to keep an eye on and safeguard the property. Homeowners can receive real-time messages and alerts from these systems on possible security breaches, fire threats, gas leaks, and other safety concerns. Smart home security systems can distinguish between typical activity and questionable conduct by utilizing data analytics and machine learning. This reduces false alerts and improves overall efficiency.Homeowners may now remotely control and monitor their properties thanks to the integration of voice assistants and mobile applications, which further improves user experience. These systems can automatically notify emer- gency contacts or local authorities in the event of an emergency, guaranteeing a prompt response. Further improvements in safety and security are anticipated as smart home technology continues to evolve; future models may have even more sophisticated predictive analytics and seamless interaction with public safety infrastructures. But these developments also give rise to worries about cybersecurity and data privacy, which calls for strong security measures to safeguard user information and guarantee the dependability of these alerting systems.
{"title":"Safety and Security Alerting in Smart Homes","authors":"K. Chennakkeshava, Hemanth Hemanth","doi":"10.55041/ijsrem36614","DOIUrl":"https://doi.org/10.55041/ijsrem36614","url":null,"abstract":"Abstract—Thanks to developments in Internet of Things (IoT) technology, safety and security alerting in smart homes has become a fundamental component of contemporary living. Smart houses are outfitted with an array of networked gadgets, in- cluding cameras, sensors, locks, and alarms, which collaborate to keep an eye on and safeguard the property. Homeowners can receive real-time messages and alerts from these systems on possible security breaches, fire threats, gas leaks, and other safety concerns. Smart home security systems can distinguish between typical activity and questionable conduct by utilizing data analytics and machine learning. This reduces false alerts and improves overall efficiency.Homeowners may now remotely control and monitor their properties thanks to the integration of voice assistants and mobile applications, which further improves user experience. These systems can automatically notify emer- gency contacts or local authorities in the event of an emergency, guaranteeing a prompt response. Further improvements in safety and security are anticipated as smart home technology continues to evolve; future models may have even more sophisticated predictive analytics and seamless interaction with public safety infrastructures. But these developments also give rise to worries about cybersecurity and data privacy, which calls for strong security measures to safeguard user information and guarantee the dependability of these alerting systems.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"123 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141820154","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}
Abstract—The following paper considers an in-depth study of face detection and classification using a pre-trained VGG16 model with a prime focus on separating real from fake facial images. Face detection is a very fundamental task in computer vision and of key importance in various security- and biometric identification-related applications, social media, and so on, in which the above-mentioned Dortania et al. findings will find their use. The idea is to use transfer learning by tuning an already trained VGG16 that was developed for large-scale image classification to do well in a specific task of face authenticity verification. For this purpose, we constructed a custom dataset with images labeled either ’real’ or ’fake’, sourced from different environments to make it diverse and hence robust. The dataset was then preprocessed by face detection using Haar cascades, resizing, normalization, and augmentation to increase the model’s capacity for generalization. This dataset was trained as well as tested on the modified VGG16 model, where only one fully connected layer at the end was changed to give an output in two classes—one for the real faces and another for the fake ones. Model performance was ascertained through training loss and accuracy in the training phase. For the 30 epochs of training, the model achieved very good training accuracy. Further performance fluctuation analysis at different epochs used detailed plots of the loss and accuracy. Testing validates further that the model is robust, having a high testing accuracy to ensure the model generalizes on unseen data. Our results show the effectiveness of transfer learning using VGG16 in face classification, where accuracy was high for the classification of real and fake faces. Thus, this study not only demonstrates the potential of pre-trained deep models in specialized applications but also shows the proper quality of the dataset and its preprocessing towards the attainment of optimal model performance. This trained model is, therefore, deployable in every real-world application where verification of faces is very important, bringing in a reliable tool for improving security and authenticity in digital relations. Index Terms—deep fake, detection, artificial intelligence, ma- chine learning, digital forensics
{"title":"Deep Fake Detection","authors":"Daksh Baveja,, Yatharth Sharma, Dr. Nagadevi S","doi":"10.55041/ijsrem36626","DOIUrl":"https://doi.org/10.55041/ijsrem36626","url":null,"abstract":"Abstract—The following paper considers an in-depth study of face detection and classification using a pre-trained VGG16 model with a prime focus on separating real from fake facial images. Face detection is a very fundamental task in computer vision and of key importance in various security- and biometric identification-related applications, social media, and so on, in which the above-mentioned Dortania et al. findings will find their use. The idea is to use transfer learning by tuning an already trained VGG16 that was developed for large-scale image classification to do well in a specific task of face authenticity verification. For this purpose, we constructed a custom dataset with images labeled either ’real’ or ’fake’, sourced from different environments to make it diverse and hence robust. The dataset was then preprocessed by face detection using Haar cascades, resizing, normalization, and augmentation to increase the model’s capacity for generalization. This dataset was trained as well as tested on the modified VGG16 model, where only one fully connected layer at the end was changed to give an output in two classes—one for the real faces and another for the fake ones. Model performance was ascertained through training loss and accuracy in the training phase. For the 30 epochs of training, the model achieved very good training accuracy. Further performance fluctuation analysis at different epochs used detailed plots of the loss and accuracy. Testing validates further that the model is robust, having a high testing accuracy to ensure the model generalizes on unseen data. Our results show the effectiveness of transfer learning using VGG16 in face classification, where accuracy was high for the classification of real and fake faces. Thus, this study not only demonstrates the potential of pre-trained deep models in specialized applications but also shows the proper quality of the dataset and its preprocessing towards the attainment of optimal model performance. This trained model is, therefore, deployable in every real-world application where verification of faces is very important, bringing in a reliable tool for improving security and authenticity in digital relations. Index Terms—deep fake, detection, artificial intelligence, ma- chine learning, digital forensics","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"48 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141819444","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 study uses past flight schedules, route data, and ticket prices to estimate airline ticket costs using machine learning regression. For ease of use and functionality, it has admin and user modules. Registering and logging in allows users to upload flight data for precise cost estimates. Ensuring continuous efficacy, the admin module makes data administration and system maintenance easier. The objective is to improve overall travel planning experiences by providing travelers with data- driven insights to help them make wise decisions and maximize the value of their airline ticket purchases. Keyword: Machine learning, Flight ticket Prediction, Flight fare.
{"title":"FLIGHT TICKET PRICE PREDICTION","authors":"Mudagal Nagarjun, R. R","doi":"10.55041/ijsrem36633","DOIUrl":"https://doi.org/10.55041/ijsrem36633","url":null,"abstract":"The study uses past flight schedules, route data, and ticket prices to estimate airline ticket costs using machine learning regression. For ease of use and functionality, it has admin and user modules. Registering and logging in allows users to upload flight data for precise cost estimates. Ensuring continuous efficacy, the admin module makes data administration and system maintenance easier. The objective is to improve overall travel planning experiences by providing travelers with data- driven insights to help them make wise decisions and maximize the value of their airline ticket purchases. Keyword: Machine learning, Flight ticket Prediction, Flight fare.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"124 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141819924","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}
This project focuses on improving the detection of network attacks by using a machine learning technique known as Long Short-Term Memory (LSTM) networks. LSTM networks are a type of neural network that excels at analyzing sequences of data, making them well-suited for identifying patterns associated with network intrusions. To enhance the LSTM model's effectiveness, we introduce new probability features that help the model better distinguish between normal and malicious activities. Our approach includes collecting network data, preprocessing it to make it suitable for training, and then using this data to train the LSTM model. We evaluate the model's performance using a range of metrics to ensure its accuracy and reliability. The results indicate that our method significantly improves the detection rate of network attacks while also reducing the number of false alarms. This means that our LSTM-based model not only catches more real threats but also makes fewer mistakes in identifying normal activities as attacks. Overall, this project showcases the potential of advanced machine learning techniques, like LSTM networks, to enhance cyber security measures and protect against network threats more effectively.
{"title":"LSTM Based New Probability Features Using Machine Learning to Improve Network Attack Detection","authors":"Er. Krishna Raj Kumar.K, Dr. S Ilangovan","doi":"10.55041/ijsrem36583","DOIUrl":"https://doi.org/10.55041/ijsrem36583","url":null,"abstract":"This project focuses on improving the detection of network attacks by using a machine learning technique known as Long Short-Term Memory (LSTM) networks. LSTM networks are a type of neural network that excels at analyzing sequences of data, making them well-suited for identifying patterns associated with network intrusions. To enhance the LSTM model's effectiveness, we introduce new probability features that help the model better distinguish between normal and malicious activities. Our approach includes collecting network data, preprocessing it to make it suitable for training, and then using this data to train the LSTM model. We evaluate the model's performance using a range of metrics to ensure its accuracy and reliability. The results indicate that our method significantly improves the detection rate of network attacks while also reducing the number of false alarms. This means that our LSTM-based model not only catches more real threats but also makes fewer mistakes in identifying normal activities as attacks. Overall, this project showcases the potential of advanced machine learning techniques, like LSTM networks, to enhance cyber security measures and protect against network threats more effectively.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"111 41","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141820298","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}
Emergency response systems must be able to promptly and accurately evaluate emergency calls. We provide a machine learning- based method in this study, called the "911 Call Analyzer," to automate the process of identifying serious crises from 911 call audio recordings. Mel- frequency cepstral coefficients (MFCCs) are used by the system to extract features, and machine learning and deep learning architectures are used for classification. To forecast the urgency and severity of each emergency call, the collected features are fed into a model that has been trained on a dataset of labelled calls. We assess the 911 Call Analyzer's performance using a test dataset, and we obtain a 91% accuracy rate with RF and XG Boost model followed by SVM with 90% accuracy, CNN with 69% accuracy and lastly LSTM with 64% accuracy. These findings show how well the suggested method works to reliably identify important crises, which helps emergency dispatchers prioritize calls and allocate resources more wisely. The 911 Call Analyzer is a tool that holds great potential for improving emergency response systems' efficacy and efficiency, which will eventually benefit those who are in need. Key Words: 911 calls, MFCCs, LSTM, CNN, SVM, RF, XG Boost.
{"title":"911 Call Analyzer: A Vital Tool for Detecting Critical Emergencies","authors":"Paresh Patil, Sushant Gaikwad, Akash Hatkangane","doi":"10.55041/ijsrem36673","DOIUrl":"https://doi.org/10.55041/ijsrem36673","url":null,"abstract":"Emergency response systems must be able to promptly and accurately evaluate emergency calls. We provide a machine learning- based method in this study, called the \"911 Call Analyzer,\" to automate the process of identifying serious crises from 911 call audio recordings. Mel- frequency cepstral coefficients (MFCCs) are used by the system to extract features, and machine learning and deep learning architectures are used for classification. To forecast the urgency and severity of each emergency call, the collected features are fed into a model that has been trained on a dataset of labelled calls. We assess the 911 Call Analyzer's performance using a test dataset, and we obtain a 91% accuracy rate with RF and XG Boost model followed by SVM with 90% accuracy, CNN with 69% accuracy and lastly LSTM with 64% accuracy. These findings show how well the suggested method works to reliably identify important crises, which helps emergency dispatchers prioritize calls and allocate resources more wisely. The 911 Call Analyzer is a tool that holds great potential for improving emergency response systems' efficacy and efficiency, which will eventually benefit those who are in need. Key Words: 911 calls, MFCCs, LSTM, CNN, SVM, RF, XG Boost.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"110 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141820440","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}
This work aims to predict, from a variety of user inputs, whether a baby will be born healthy or underweight. Taking into account characteristics including parental health, ethnicity, educational background, and region—all of which have an impact on healthcare accessibility and environmental factors—the study acknowledges the significance of birth weight in relation to gestational age. Through the examination of extensive datasets containing these lifestyle and demographic characteristics, health care providers can improve prenatal care and interventions, concentrating more carefully on populations that are at risk. With the help of user-supplied data, this prediction tool provides a probabilistic estimate of birth weight outcomes, giving parents and medical professionals peace of mind and assistance. Keyword: Low Birth weight (LBW), Smart health informatics, Machine Learning (ML).
{"title":"EARLY PREDICTION OF LOWBIRTH WEIGHT CASES USING ML","authors":"K. M, M. G L","doi":"10.55041/ijsrem36637","DOIUrl":"https://doi.org/10.55041/ijsrem36637","url":null,"abstract":"This work aims to predict, from a variety of user inputs, whether a baby will be born healthy or underweight. Taking into account characteristics including parental health, ethnicity, educational background, and region—all of which have an impact on healthcare accessibility and environmental factors—the study acknowledges the significance of birth weight in relation to gestational age. Through the examination of extensive datasets containing these lifestyle and demographic characteristics, health care providers can improve prenatal care and interventions, concentrating more carefully on populations that are at risk. With the help of user-supplied data, this prediction tool provides a probabilistic estimate of birth weight outcomes, giving parents and medical professionals peace of mind and assistance. Keyword: Low Birth weight (LBW), Smart health informatics, Machine Learning (ML).","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"77 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141819197","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}
Generative Adversarial Networks (GANs) have emerged as a powerful class of generative models, capable of synthesizing realistic images by leveraging adversarial training. It explores the process of building a Generative Adversarial Network for image synthesis, delving into the underlying architecture, training methodology, and potential applications. Generative Adversarial Networks typically run unsupervised and use a cooperative zero- sum game framework to learn, where one person's gain equals another person's loss. The proposed Generative Adversarial Network architecture consists of a generator network that learns to create images from random noise and a discriminator network trained to distinguish between real and generated images. Through an adversarial training process, these networks iteratively refine their capabilities, resulting in a generator that produces increasingly realistic pictures and a discriminator with enhanced discriminative abilities. Generative Adversarial Networks are an effective tool for producing realistic, high-quality outputs in a variety of fields, including text and image generation, because of this back-and- forth competition, which results in the creation of increasingly convincing and indistinguishable synthetic data.
{"title":"Building a Generative Adversarial Network for Image Synthesis","authors":"B. Y. Chandra","doi":"10.55041/ijsrem36641","DOIUrl":"https://doi.org/10.55041/ijsrem36641","url":null,"abstract":"Generative Adversarial Networks (GANs) have emerged as a powerful class of generative models, capable of synthesizing realistic images by leveraging adversarial training. It explores the process of building a Generative Adversarial Network for image synthesis, delving into the underlying architecture, training methodology, and potential applications. Generative Adversarial Networks typically run unsupervised and use a cooperative zero- sum game framework to learn, where one person's gain equals another person's loss. The proposed Generative Adversarial Network architecture consists of a generator network that learns to create images from random noise and a discriminator network trained to distinguish between real and generated images. Through an adversarial training process, these networks iteratively refine their capabilities, resulting in a generator that produces increasingly realistic pictures and a discriminator with enhanced discriminative abilities. Generative Adversarial Networks are an effective tool for producing realistic, high-quality outputs in a variety of fields, including text and image generation, because of this back-and- forth competition, which results in the creation of increasingly convincing and indistinguishable synthetic data.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"7 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141819692","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 goal of the machine learning-powered e- commerce product recommendation system is to provide a complete, end-to-end web-based platform that improves online shopping by making insightful product recommendations. This system has features for administrators as well as users, and safe access requires login credentials. To extract information from product photos, the system's backend uses machine learning models, specifically convolutional neural networks (CNNs) for image analysis. The user's buying experience is enhanced by the use of sophisticated machine learning techniques, which guarantee relevant and accurate recommendations. To sum up, our study highlights how important machine learning-driven recommendation systems are for increasing consumer engagement and generating income for e-commerce platforms. Through constant innovation and improvement, we strive to provide businesses with state-of-the-art resources to enable them to provide individualized and significant purchasing experiences. Key Words: User Experience, Product Recommendation, Neural Network (CNN’s)
{"title":"E-Commerce Product Recommendation System Using Machine Learning","authors":"Darshan M, A. C","doi":"10.55041/ijsrem36656","DOIUrl":"https://doi.org/10.55041/ijsrem36656","url":null,"abstract":"The goal of the machine learning-powered e- commerce product recommendation system is to provide a complete, end-to-end web-based platform that improves online shopping by making insightful product recommendations. This system has features for administrators as well as users, and safe access requires login credentials. To extract information from product photos, the system's backend uses machine learning models, specifically convolutional neural networks (CNNs) for image analysis. The user's buying experience is enhanced by the use of sophisticated machine learning techniques, which guarantee relevant and accurate recommendations. To sum up, our study highlights how important machine learning-driven recommendation systems are for increasing consumer engagement and generating income for e-commerce platforms. Through constant innovation and improvement, we strive to provide businesses with state-of-the-art resources to enable them to provide individualized and significant purchasing experiences. Key Words: User Experience, Product Recommendation, Neural Network (CNN’s)","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"19 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141819281","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 health of ecosystems, land use, and agriculture are all seriously threatened by soil erosion.This innovative method makes use of sophisticated image processing techniques like contour evaluation, adaptive thresholding, Gaussian blur, and morphological operations to analyze aerial photos. The method increases the accuracy of detecting erosion and identifies susceptible areas in expansive landscapes. This scalable approach offers a potent weapon in the fight against soil degradation and promises to transform ecological monitoring and management. The novel approach represents a significant development in the evaluation of soil erosion andenvironmental preservation. Keyword: Soil Erosion, Aerial Photography, Image Processing, Gaussian Blur.
{"title":"INNOVATIVE AERIAL IMAGE PROCESSING TECHNIQUES FOR ENHANCED SOIL EROSION DETECTION","authors":"Nikhil A, R. R","doi":"10.55041/ijsrem36676","DOIUrl":"https://doi.org/10.55041/ijsrem36676","url":null,"abstract":"The health of ecosystems, land use, and agriculture are all seriously threatened by soil erosion.This innovative method makes use of sophisticated image processing techniques like contour evaluation, adaptive thresholding, Gaussian blur, and morphological operations to analyze aerial photos. The method increases the accuracy of detecting erosion and identifies susceptible areas in expansive landscapes. This scalable approach offers a potent weapon in the fight against soil degradation and promises to transform ecological monitoring and management. The novel approach represents a significant development in the evaluation of soil erosion andenvironmental preservation. Keyword: Soil Erosion, Aerial Photography, Image Processing, Gaussian Blur.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"14 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141819398","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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