The food industry has led the agricultural economy of the state all India to prosperity. India has historically been the largest producing nation having identity of Agricultural Land. Grains , fruits , Vegetables , such as potatoes, oranges, Tomato ,sugarcane and other specially grains and cottons are the chief crops of the India. Citrus and cotton industries have been a driving force behind Maharashtra's impressive economic growth.. The situation has created job opportunities for many people, boosting the state's economic potential. To maintain the prosperity of citrus and cotton industries, Government has been concerned about disease control, labour cost, and global market. During the recent past, citrus canker and citrus greening, Black spot-n cotton has become serious threats to citrus in Maharashtra. Infection by these diseases weakens trees, leading to decline, mortality, lower yields, and decreased commercial value. Likewise, the farmers are concerned about costs from tree loss, scouting, and chemicals used in an attempt to control the disease. An automated detection system may help in prevention and, thus reduce the serious loss to the industries, farmers and Economy of country. This research aims to the development of disease detection with pattern recognition approaches for these diseases in crop. The detection approach consists of three major sub-systems, namely, image acquisition, image processing and pattern recognition. The imaging processing sub-system includes image preprocessing for background noise removal, leaf boundary detection and image feature extraction. Pattern recognition approaches will be use to classify samples among several different conditions on crops. In order to evaluate the classification approaches, results will be compared between classification methods for the different induvial fruits, vegetable, grains disease detection. Obtained results will help in demonstration of classification accuracy which is targeted as better than existing for proposed model as high as 97.00%. This study aimed to assess the potential of identifying plant diseases by examining visible signs on fruits and leaves. These data collection and initial knowledge acquisition is plan in offline approaches. By implementing this simple model, we can achieve a more favourable cost-to-production ratio compared to complex solutions.
{"title":"Revolutionizing Plant Disease Detection: A Review of Deep Learning and Machine Learning Algorithms","authors":"Ekta Kapase, Prem Bhandari, Atharva Bodake, Ujwal Chaudhari","doi":"10.32628/ijsrset2411227","DOIUrl":"https://doi.org/10.32628/ijsrset2411227","url":null,"abstract":"The food industry has led the agricultural economy of the state all India to prosperity. India has historically been the largest producing nation having identity of Agricultural Land. Grains , fruits , Vegetables , such as potatoes, oranges, Tomato ,sugarcane and other specially grains and cottons are the chief crops of the India. Citrus and cotton industries have been a driving force behind Maharashtra's impressive economic growth.. The situation has created job opportunities for many people, boosting the state's economic potential. To maintain the prosperity of citrus and cotton industries, Government has been concerned about disease control, labour cost, and global market. \u0000During the recent past, citrus canker and citrus greening, Black spot-n cotton has become serious threats to citrus in Maharashtra. Infection by these diseases weakens trees, leading to decline, mortality, lower yields, and decreased commercial value. Likewise, the farmers are concerned about costs from tree loss, scouting, and chemicals used in an attempt to control the disease. An automated detection system may help in prevention and, thus reduce the serious loss to the industries, farmers and Economy of country. \u0000This research aims to the development of disease detection with pattern recognition approaches for these diseases in crop. The detection approach consists of three major sub-systems, namely, image acquisition, image processing and pattern recognition. The imaging processing sub-system includes image preprocessing for background noise removal, leaf boundary detection and image feature extraction. Pattern recognition approaches will be use to classify samples among several different conditions on crops. \u0000In order to evaluate the classification approaches, results will be compared between classification methods for the different induvial fruits, vegetable, grains disease detection. Obtained results will help in demonstration of classification accuracy which is targeted as better than existing for proposed model as high as 97.00%. This study aimed to assess the potential of identifying plant diseases by examining visible signs on fruits and leaves. These data collection and initial knowledge acquisition is plan in offline approaches. By implementing this simple model, we can achieve a more favourable cost-to-production ratio compared to complex solutions.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"62 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140713577","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 research proposes a video-based system for analyzing human squats and providing real-time feedback to improve posture. The system leverages MediaPipe, an open-source pose estimation library, to identify key body joints during squats. By calculating crucial joint angles (knee flexion, hip flexion, ankle dorsiflexion), the system assesses squat form against established biomechanical principles. Deviations from these principles trigger real-time feedback messages or visual cues to guide users towards optimal squat posture. The paper details the system architecture, with a client-side application performing pose estimation and feedback generation. The methodology outlines data collection with various squat variations, system development integrating MediaPipe, and evaluation through user testing with comparison to expert evaluations. Key features include real-time feedback and customizable thresholds for user adaptation. Potential applications encompass fitness training, physical therapy, and sports training. Finally, the paper explores future work possibilities like mobile integration, advanced feedback mechanisms, and machine learning for automatic threshold adjustments. This research offers a valuable tool for squat analysis, empowering users to achieve their fitness goals with proper form and reduced injury risk.
{"title":"AI Trainer : Video-Based Squat Analysis","authors":"Prof. Anuja Garande, Kushank Patil, Rasika Deshmukh, Siddhi Gurav, Chaitanya Yadav","doi":"10.32628/ijsrset2411221","DOIUrl":"https://doi.org/10.32628/ijsrset2411221","url":null,"abstract":"This research proposes a video-based system for analyzing human squats and providing real-time feedback to improve posture. The system leverages MediaPipe, an open-source pose estimation library, to identify key body joints during squats. By calculating crucial joint angles (knee flexion, hip flexion, ankle dorsiflexion), the system assesses squat form against established biomechanical principles. Deviations from these principles trigger real-time feedback messages or visual cues to guide users towards optimal squat posture. The paper details the system architecture, with a client-side application performing pose estimation and feedback generation. The methodology outlines data collection with various squat variations, system development integrating MediaPipe, and evaluation through user testing with comparison to expert evaluations. Key features include real-time feedback and customizable thresholds for user adaptation. Potential applications encompass fitness training, physical therapy, and sports training. Finally, the paper explores future work possibilities like mobile integration, advanced feedback mechanisms, and machine learning for automatic threshold adjustments. This research offers a valuable tool for squat analysis, empowering users to achieve their fitness goals with proper form and reduced injury risk.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"15 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140732586","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 current study deals with the objective “How to Consumer Preception Regarding Demand of Branded Shoes”. In this study we observed that how to consumer perception impact on demand for branded products like shoes. in this study also to know how culture, income, social status, life style, Peer pressure and other factors impact on the demand of branded shoes. In the modern era technology is growing fastly. They directly affect on our thinking process and they result represent our demand is also change as per time to time. We can say that our perception related to product change very soon. In this study we discuss only some points. The information of the study could be further used by the researchers and practitioners for conducting future studies in the similar area.
{"title":"Consumer Perception Regarding Demand of Branded Shoes","authors":"Deepak Kumar Chachda, Dr Kavita A. Jain","doi":"10.32628/ijsrset2411223","DOIUrl":"https://doi.org/10.32628/ijsrset2411223","url":null,"abstract":"The current study deals with the objective “How to Consumer Preception Regarding Demand of Branded Shoes”. In this study we observed that how to consumer perception impact on demand for branded products like shoes. in this study also to know how culture, income, social status, life style, Peer pressure and other factors impact on the demand of branded shoes. In the modern era technology is growing fastly. They directly affect on our thinking process and they result represent our demand is also change as per time to time. We can say that our perception related to product change very soon. In this study we discuss only some points. The information of the study could be further used by the researchers and practitioners for conducting future studies in the similar area.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"18 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140733251","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}
Ameen Hafeez, Rohith M K, Sakshi Prashant, Sinchana Hegde, Prof. Shwetha K S
Lip reading is a complex but interesting path for the growth of speech recognition algorithms. It is the ability of deciphering spoken words by evaluating visual cues from lip movements. In this study, we suggest a unique method for lip reading that converts lip motions into textual representations by using deep neural networks. Convolutional neural networks are used in the methodology to extract visual features, recurrent neural networks are used to simulate temporal context, and the Connectionist Temporal Classification loss function is used to align lip features with corresponding phonemes. The study starts with a thorough investigation of data loading methods, which include alignment extraction and video preparation. A well selected dataset with video clips and matching phonetic alignments is presented. We select relevant face regions, convert frames to grayscale, then standardize the resulting data so that it can be fed into a neural network. The neural network architecture is presented in depth, displaying a series of bidirectional LSTM layers for temporal context understanding after 3D convolutional layers for spatial feature extraction. Careful consideration of input shapes, layer combinations, and parameter selections forms the foundation of the model's design. To train the model, we align predicted phoneme sequences with ground truth alignments using the CTC loss. Dynamic learning rate scheduling and a unique callback mechanism for training visualization of predictions are integrated into the training process. After training on a sizable dataset, the model exhibits remarkable convergence and proves its capacity to understand intricate temporal correlations. Through the use of both quantitative and qualitative evaluations, the results are thoroughly assessed. We visually check the model's lip reading abilities and assess its performance using common speech recognition criteria. It is explored how different model topologies and hyperparameters affect performance, offering guidance for future research. The trained model is tested on external video samples to show off its practical application. Its accuracy and resilience in lip-reading spoken phrases are demonstrated. By providing a deep learning framework for precise and effective speech recognition, this research adds to the rapidly changing field of lip reading devices. The results offer opportunities for additional development and implementation in various fields, such as assistive technologies, audio-visual communication systems, and human-computer interaction.
{"title":"Survey on Silentinterpreter : Analysis of Lip Movement and Extracting Speech using Deep Learning","authors":"Ameen Hafeez, Rohith M K, Sakshi Prashant, Sinchana Hegde, Prof. Shwetha K S","doi":"10.32628/ijsrset2411219","DOIUrl":"https://doi.org/10.32628/ijsrset2411219","url":null,"abstract":"Lip reading is a complex but interesting path for the growth of speech recognition algorithms. It is the ability of deciphering spoken words by evaluating visual cues from lip movements. In this study, we suggest a unique method for lip reading that converts lip motions into textual representations by using deep neural networks. Convolutional neural networks are used in the methodology to extract visual features, recurrent neural networks are used to simulate temporal context, and the Connectionist Temporal Classification loss function is used to align lip features with corresponding phonemes.\u0000The study starts with a thorough investigation of data loading methods, which include alignment extraction and video preparation. A well selected dataset with video clips and matching phonetic alignments is presented. We select relevant face regions, convert frames to grayscale, then standardize the resulting data so that it can be fed into a neural network.\u0000The neural network architecture is presented in depth, displaying a series of bidirectional LSTM layers for temporal context understanding after 3D convolutional layers for spatial feature extraction. Careful consideration of input shapes, layer combinations, and parameter selections forms the foundation of the model's design. To train the model, we align predicted phoneme sequences with ground truth alignments using the CTC loss.\u0000Dynamic learning rate scheduling and a unique callback mechanism for training visualization of predictions are integrated into the training process. After training on a sizable dataset, the model exhibits remarkable convergence and proves its capacity to understand intricate temporal correlations.\u0000Through the use of both quantitative and qualitative evaluations, the results are thoroughly assessed. We visually check the model's lip reading abilities and assess its performance using common speech recognition criteria. It is explored how different model topologies and hyperparameters affect performance, offering guidance for future research.\u0000The trained model is tested on external video samples to show off its practical application. Its accuracy and resilience in lip-reading spoken phrases are demonstrated.\u0000By providing a deep learning framework for precise and effective speech recognition, this research adds to the rapidly changing field of lip reading devices. The results offer opportunities for additional development and implementation in various fields, such as assistive technologies, audio-visual communication systems, and human-computer interaction.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"14 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140732702","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}
In agriculture, Internet of Things means using smart devices like sensors and cameras to gather information about crops, soil and weather. This data helps farmers make better decisions about watering, fertilizing, and protecting their plants. By connecting everything to the internet, farmers can monitor their fields remotely and take action quickly when needed, leading to healthier crops and higher yields. This paper analysis the IoT have developed new device for smart agriculture. The first how IoT works and which device are used for smart farming and then application of IoT in agriculture like precision farming, Livestock Monitoring, Alerts and notification, Crop health monitoring, Sprinkler. Then which IoT device used in agriculture. So, the use of IoT in agriculture will be covered in this paper.
{"title":"Internet of Things in Agriculture : A Review","authors":"Navoday Atul Kopawar, Komal Gajanan Wankhede","doi":"10.32628/ijsrset2411215","DOIUrl":"https://doi.org/10.32628/ijsrset2411215","url":null,"abstract":"In agriculture, Internet of Things means using smart devices like sensors and cameras to gather information about crops, soil and weather. This data helps farmers make better decisions about watering, fertilizing, and protecting their plants. By connecting everything to the internet, farmers can monitor their fields remotely and take action quickly when needed, leading to healthier crops and higher yields. This paper analysis the IoT have developed new device for smart agriculture. The first how IoT works and which device are used for smart farming and then application of IoT in agriculture like precision farming, Livestock Monitoring, Alerts and notification, Crop health monitoring, Sprinkler. Then which IoT device used in agriculture. So, the use of IoT in agriculture will be covered in this paper. ","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140735547","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}
Sourabh Pawar, Pranav More, Tejas Pawar, Prof. Priti Rathod
Heart complaint is a major global health concern, especially in prognosticating cardiovascular issues. Machine literacy (ML) and the Internet of effects (IoT) offer new ways to dissect healthcare data. still, current exploration lacks depth in using ML for heart complaint vaticination. To fill this gap, we propose a unique system that uses ML to identify crucial features for better heart complaint vaticination delicacy. Our model combines colorful features and bracket ways to achieve an delicacy of 88.7 in prognosticating heart complaint, with the cold-blooded arbitrary timber and direct model (HRFLM) proving particularly effective. This study advances heart complaint discovery by integrating ML and IoT technologies.
心脏病是全球关注的主要健康问题，尤其是在心血管问题的预后方面。机器扫盲（ML）和物联网（IoT）提供了剖析医疗保健数据的新方法。为了填补这一空白，我们提出了一种独特的系统，利用 ML 来识别关键特征，从而更好地进行心脏疾病诊断。我们的模型结合了丰富多彩的特征和支架方法，在预报心脏病方面达到了 88.7 的精确度，其中冷血任意木材和直接模型（HRFLM）尤其有效。这项研究通过整合 ML 和 IoT 技术，推动了心脏疾病的发现。
{"title":"Cardiovascular Disease Long-Term Care Risk Prediction by Claims Data Analysis Using Machine Learning","authors":"Sourabh Pawar, Pranav More, Tejas Pawar, Prof. Priti Rathod","doi":"10.32628/ijsrset2411222","DOIUrl":"https://doi.org/10.32628/ijsrset2411222","url":null,"abstract":"Heart complaint is a major global health concern, especially in prognosticating cardiovascular issues. Machine literacy (ML) and the Internet of effects (IoT) offer new ways to dissect healthcare data. still, current exploration lacks depth in using ML for heart complaint vaticination. To fill this gap, we propose a unique system that uses ML to identify crucial features for better heart complaint vaticination delicacy. Our model combines colorful features and bracket ways to achieve an delicacy of 88.7 in prognosticating heart complaint, with the cold-blooded arbitrary timber and direct model (HRFLM) proving particularly effective. This study advances heart complaint discovery by integrating ML and IoT technologies.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"29 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140734440","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}
With the rapid expansion of the internet, there has been an exponential surge in data volume, encompassing a myriad of documents laden with diverse types of information. This vast expanse includes structured and unstructured data, ranging from big data sets to formatted text and unformatted content. However, this abundance of unstructured data poses significant challenges in terms of effective management. Manual classification of this burgeoning data landscape is impractical, necessitating automated solutions. In this paper, we propose leveraging advanced machine learning techniques, particularly the BERT model, to classify documents based on contextual understanding, offering a more efficient and accurate approach to handling the data deluge.
{"title":"Multiclass Document Classifier using BERT","authors":"Shruti A. Gadewar, Prof. P. H. Pawar","doi":"10.32628/ijsrset241127","DOIUrl":"https://doi.org/10.32628/ijsrset241127","url":null,"abstract":"With the rapid expansion of the internet, there has been an exponential surge in data volume, encompassing a myriad of documents laden with diverse types of information. This vast expanse includes structured and unstructured data, ranging from big data sets to formatted text and unformatted content. However, this abundance of unstructured data poses significant challenges in terms of effective management. Manual classification of this burgeoning data landscape is impractical, necessitating automated solutions. In this paper, we propose leveraging advanced machine learning techniques, particularly the BERT model, to classify documents based on contextual understanding, offering a more efficient and accurate approach to handling the data deluge.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"28 42","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140373006","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 Farmer Exchange Rate (NTP) is the ratio of the price index received by farmers (It) to the price index paid by farmers (Ib). This research aims to understand the calculation of NTP and analyze the faktors influencing NTP. The study focuses on East Kalimantan Province, where in 2022, there was an increase in NTP due to a 6.34% rise in the Farmers' Received Price Index (It), while the Farmers' Paid Price Index (Ib) only increased by 1.89%. Given various faktors affecting the subsector of farmers' exchange rates in East Kalimantan, the research employs faktor analysis—a statistical tool to reduce influencing faktors to a set of indicators without significant information loss. The variables for the subsector Nilai Tukar Petani (NTP) include food crops, holticulture, smallholder plantation crops, livestock, and fisheries.
{"title":"Analysis of Factors that Influence the Price Index Received by Farmers in East Kalimantan","authors":"Muklas Rivai, Cici Safitri, Lorena Uli Tara Nainggolan, Muhammad Dzaki Al Khawarizmi, Mutia Putri Apsari, Nadia Apriliani, Putri Annur Syakilla, Ranissa Sianturi","doi":"10.32628/ijsrset241128","DOIUrl":"https://doi.org/10.32628/ijsrset241128","url":null,"abstract":"The Farmer Exchange Rate (NTP) is the ratio of the price index received by farmers (It) to the price index paid by farmers (Ib). This research aims to understand the calculation of NTP and analyze the faktors influencing NTP. The study focuses on East Kalimantan Province, where in 2022, there was an increase in NTP due to a 6.34% rise in the Farmers' Received Price Index (It), while the Farmers' Paid Price Index (Ib) only increased by 1.89%. Given various faktors affecting the subsector of farmers' exchange rates in East Kalimantan, the research employs faktor analysis—a statistical tool to reduce influencing faktors to a set of indicators without significant information loss. The variables for the subsector Nilai Tukar Petani (NTP) include food crops, holticulture, smallholder plantation crops, livestock, and fisheries.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"75 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140371284","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}
Potholes are damage caused to the ground by the formation of water and wear and tear over time. According to statistical data, bad road conditions account for about one- third of the total road accidents which has been increasing exponentially. Potholes have become so common that it has become second nature for people to learn how to spot and avoid them, which causes further accidents. The need of the hour is to build a dependable pothole detection system to accurately detect potholes and warn the drivers and government officials in advance. The process to build such a system is divided into two steps i.e. collection of data and pothole identification. The first step is achieved by taking the data from already available data sets on the Internet. The other step includes labeling the potholes in the data set which is usually done manually. This paper focuses mainly on Visual-based techniques to identify the best detection method by comparing popular Machine Learning models and algorithms. The obtained data set is trained using various transfer learning techniques like You Only Look Once (YOLO)[1] and Single Shot Detector (SSD) [1]. Apart from transfer learning, this paper also focuses on some proposed techniques using Convolutional Neural Net- works (CNN) and classification algorithms like Support Vector Machine (SVM)[21] to identify and localize potholes. The actual size of potholes is calculated using morphological operations, which is a just a straightforward technique to analyze figures using set theory. To analyze every model and find the best model, each model is trained on different sizes of data sets and the obtained result is validated and examined by considering different aspects like speed and accuracy in mind.
{"title":"Pothole Detection Using Machine Learning Models","authors":"Mayank Dhingra, Rahul Dhingra, Meghna Sharma","doi":"10.32628/ijsrset241126","DOIUrl":"https://doi.org/10.32628/ijsrset241126","url":null,"abstract":"Potholes are damage caused to the ground by the formation of water and wear and tear over time. According to statistical data, bad road conditions account for about one- third of the total road accidents which has been increasing exponentially. Potholes have become so common that it has become second nature for people to learn how to spot and avoid them, which causes further accidents. The need of the hour is to build a dependable pothole detection system to accurately detect potholes and warn the drivers and government officials in advance. The process to build such a system is divided into two steps i.e. collection of data and pothole identification. The first step is achieved by taking the data from already available data sets on the Internet. The other step includes labeling the potholes in the data set which is usually done manually. This paper focuses mainly on Visual-based techniques to identify the best detection method by comparing popular Machine Learning models and algorithms. The obtained data set is trained using various transfer learning techniques like You Only Look Once (YOLO)[1] and Single Shot Detector (SSD) [1]. Apart from transfer learning, this paper also focuses on some proposed techniques using Convolutional Neural Net- works (CNN) and classification algorithms like Support Vector Machine (SVM)[21] to identify and localize potholes. The actual size of potholes is calculated using morphological operations, which is a just a straightforward technique to analyze figures using set theory. To analyze every model and find the best model, each model is trained on different sizes of data sets and the obtained result is validated and examined by considering different aspects like speed and accuracy in mind.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"34 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140372522","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}
Time series analysis enables the identification of trends and patterns in data, allowing for the development of forecasting models that predict future values. One effective approach for forecasting seasonal time series data is the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. Bagging Exponential Smoothing with STL Decomposition (BES-STL) is an ensemble machine learning method aimed at enhancing forecasting accuracy. STL Method, which stands for Seasonal-Trend decomposition using Loess, is utilized to decompose time series data into three components, namely trend, seasonal, and remainder components. In the remainder component, the process of bootstrap aggregation (bagging) with Moving Block Bootstrapping (MBB) is used to obtain synthetic data, followed by averaging the value by month from the entire series as the forecast results. A comparative analysis was conducted using the SARIMA, BES-STL, and BES-RSTL models. The optimal model, with the lowest MAPE and RMSE, is then implemented to predict national red chili production. The results indicate that the SARIMA(1,1,1)(0,1,1)12 model has the best performance with a MAPE of 7.06 and a RMSE of 95,473. The top-performing model is utilized to forecast data from January to December 2022. Additionally, the forecasted results are compared to the actual data, resulting in a highly precise MAPE of 5.39.
{"title":"Comparison of SARIMA, Bagging Exponential Smoothing with STL Decomposition and Robust STL Decomposition for Forecasting Red Chili Production","authors":"Titin Agustina, Anwar Fitrianto, Indahwati","doi":"10.32628/ijsrset2411146","DOIUrl":"https://doi.org/10.32628/ijsrset2411146","url":null,"abstract":"Time series analysis enables the identification of trends and patterns in data, allowing for the development of forecasting models that predict future values. One effective approach for forecasting seasonal time series data is the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. Bagging Exponential Smoothing with STL Decomposition (BES-STL) is an ensemble machine learning method aimed at enhancing forecasting accuracy. STL Method, which stands for Seasonal-Trend decomposition using Loess, is utilized to decompose time series data into three components, namely trend, seasonal, and remainder components. In the remainder component, the process of bootstrap aggregation (bagging) with Moving Block Bootstrapping (MBB) is used to obtain synthetic data, followed by averaging the value by month from the entire series as the forecast results. A comparative analysis was conducted using the SARIMA, BES-STL, and BES-RSTL models. The optimal model, with the lowest MAPE and RMSE, is then implemented to predict national red chili production. The results indicate that the SARIMA(1,1,1)(0,1,1)12 model has the best performance with a MAPE of 7.06 and a RMSE of 95,473. The top-performing model is utilized to forecast data from January to December 2022. Additionally, the forecasted results are compared to the actual data, resulting in a highly precise MAPE of 5.39.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":" 1114","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140382603","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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