Despite rapid population growth, agriculture feeds everyone. To feed the people, agriculture must detect plant illnesses early. Predicting crop diseases early is unfortunate. The publication educates farmers about cutting-edge plant leaf disease-reduction strategies. Since tomato is a readily accessible vegetable, machine learning and image processing with an accurate algorithm are used to identify tomato leaf illnesses. This study examines disordered tomato leaf samples. Based on early signs, farmers may quickly identify tomato leaf problem samples. Histogram Equalization improves tomato leaf samples after re sizing them to 256 × 256 pixels. K-means clustering divides data space into Voronoi cells. Contour tracing extracts leaf sample boundaries. Discrete Wavelet Transform, Principal Component Analysis, and Grey Level Co-occurrence Matrix retrieve leaf sample information.
{"title":"Investigation of early symptoms of tomato leaf disorder by using analysing image and deep learning models","authors":"Surendra Reddy Vinta, Ashok Kumar Koshariya, Sampath Kumar S, Aditya, Annantharao Gottimukkala","doi":"10.4108/eetiot.4815","DOIUrl":"https://doi.org/10.4108/eetiot.4815","url":null,"abstract":"Despite rapid population growth, agriculture feeds everyone. To feed the people, agriculture must detect plant illnesses early. Predicting crop diseases early is unfortunate. The publication educates farmers about cutting-edge plant leaf disease-reduction strategies. Since tomato is a readily accessible vegetable, machine learning and image processing with an accurate algorithm are used to identify tomato leaf illnesses. This study examines disordered tomato leaf samples. Based on early signs, farmers may quickly identify tomato leaf problem samples. Histogram Equalization improves tomato leaf samples after re sizing them to 256 × 256 pixels. K-means clustering divides data space into Voronoi cells. Contour tracing extracts leaf sample boundaries. Discrete Wavelet Transform, Principal Component Analysis, and Grey Level Co-occurrence Matrix retrieve leaf sample information.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"6 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139439989","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}
Research in agriculture is expanding. Agriculture in particular relies heavily on earth and environmental factors, such as temperature, humidity, and rainfall, to forecast crops. Crop prediction is a crucial problem in agriculture, and machine learning is an emerging study area in this area. Any grower is curious to know how much of a harvest he can anticipate. In the past, producers had control over the selection of the product to be grown, the monitoring of its development, and the timing of its harvest. Today, however, the agricultural community finds it challenging to carry on because of the sudden shifts in the climate. As a result, machine learning techniques have increasingly replaced traditional prediction methods. These techniques have been employed in this research to determine crop production. It is critical to use effective feature selection techniques to transform the raw data into a dataset that is machine learning compatible in order to guarantee that a particular machine learning (ML) model operates with a high degree of accuracy. The accuracy of the model will increase by reducing redundant data and using only data characteristics that are highly pertinent in determining the model's final output. In order to guarantee that only the most important characteristics are included in the model, it is necessary to use optimal feature selection. Our model will become overly complex if we combine every characteristic from the raw data without first examining their function in the model-building process. Additionally, the time and area complexity of the Machine learning model will grow with the inclusion of new characteristics that have little impact on the model's performance. The findings show that compared to the current classification method, an ensemble technique provides higher prediction accuracy.
{"title":"Crop Growth Prediction using Ensemble KNN-LR Model","authors":"Attaluri Harshitha, Beebi Naseeba, Narendra Kumar Rao, Abbaraju Sai Sathwik, Nagendra Panini Challa","doi":"10.4108/eetiot.4814","DOIUrl":"https://doi.org/10.4108/eetiot.4814","url":null,"abstract":"Research in agriculture is expanding. Agriculture in particular relies heavily on earth and environmental factors, such as temperature, humidity, and rainfall, to forecast crops. Crop prediction is a crucial problem in agriculture, and machine learning is an emerging study area in this area. Any grower is curious to know how much of a harvest he can anticipate. In the past, producers had control over the selection of the product to be grown, the monitoring of its development, and the timing of its harvest. Today, however, the agricultural community finds it challenging to carry on because of the sudden shifts in the climate. As a result, machine learning techniques have increasingly replaced traditional prediction methods. These techniques have been employed in this research to determine crop production. It is critical to use effective feature selection techniques to transform the raw data into a dataset that is machine learning compatible in order to guarantee that a particular machine learning (ML) model operates with a high degree of accuracy. The accuracy of the model will increase by reducing redundant data and using only data characteristics that are highly pertinent in determining the model's final output. In order to guarantee that only the most important characteristics are included in the model, it is necessary to use optimal feature selection. Our model will become overly complex if we combine every characteristic from the raw data without first examining their function in the model-building process. Additionally, the time and area complexity of the Machine learning model will grow with the inclusion of new characteristics that have little impact on the model's performance. The findings show that compared to the current classification method, an ensemble technique provides higher prediction accuracy.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"3 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139439070","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}
Companies constantly strive to retain their professional employees to minimize the expenses associated with recruiting and training new staff members. Accurately anticipating whether a particular employee is likely to leave or remain with the company can empower the organization to take proactive measures. Unlike physical systems, human resource challenges cannot be encapsulated by precise scientific or analytical formulas. Consequently, machine learning techniques emerge as the most effective tools for addressing this objective. In this paper, we present a comprehensive approach for predicting employee attrition using machine learning, ensemble techniques, and deep learning, applied to the IBM Watson dataset. We employed a diverse set of classifiers, including Logistic regression classifier, K-nearest neighbour (KNN), Decision Tree, Naïve Bayes, Gradient boosting, AdaBoost, Random Forest, Stacking, XG Boost, “FNN (Feedforward Neural Network)”, and “CNN (Convolutional Neural Network)” on the dataset. Our most successful model, which harnesses a deep learning technique known as FNN, achieved superior predictive performance with highest Accuracy, recall and F1-score of 97.5%, 83.93% and 91.26%.
企业一直在努力留住专业员工，以尽量减少招聘和培训新员工的相关费用。准确预测某位员工可能离职还是继续留在公司，可以让企业有能力采取积极措施。与物理系统不同，人力资源挑战无法用精确的科学或分析公式来概括。因此，机器学习技术成为实现这一目标的最有效工具。在本文中，我们介绍了一种利用机器学习、集合技术和深度学习预测员工流失的综合方法，并将其应用于 IBM Watson 数据集。我们在数据集上使用了多种分类器，包括逻辑回归分类器、K-近邻（KNN）、决策树、奈夫贝叶斯、梯度提升、AdaBoost、随机森林、堆叠、XG Boost、"FNN（前馈神经网络）"和 "CNN（卷积神经网络）"。我们最成功的模型利用了一种名为 FNN 的深度学习技术，取得了卓越的预测性能，准确率、召回率和 F1 分数分别达到 97.5%、83.93% 和 91.26%。
{"title":"Employee Attrition: Analysis of Data Driven Models","authors":"Manju Nandal, Veena Grover, Divya Sahu, Mahima Dogra","doi":"10.4108/eetiot.4762","DOIUrl":"https://doi.org/10.4108/eetiot.4762","url":null,"abstract":"Companies constantly strive to retain their professional employees to minimize the expenses associated with recruiting and training new staff members. Accurately anticipating whether a particular employee is likely to leave or remain with the company can empower the organization to take proactive measures. Unlike physical systems, human resource challenges cannot be encapsulated by precise scientific or analytical formulas. Consequently, machine learning techniques emerge as the most effective tools for addressing this objective. In this paper, we present a comprehensive approach for predicting employee attrition using machine learning, ensemble techniques, and deep learning, applied to the IBM Watson dataset. We employed a diverse set of classifiers, including Logistic regression classifier, K-nearest neighbour (KNN), Decision Tree, Naïve Bayes, Gradient boosting, AdaBoost, Random Forest, Stacking, XG Boost, “FNN (Feedforward Neural Network)”, and “CNN (Convolutional Neural Network)” on the dataset. Our most successful model, which harnesses a deep learning technique known as FNN, achieved superior predictive performance with highest Accuracy, recall and F1-score of 97.5%, 83.93% and 91.26%.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"33 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451442","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}
INTRODUCTION: Prioritizing sustainability initiatives is crucial for retail chains as they integrate Internet of Things (IoT) technologies to drive environmental responsibility. Retail chains have responsibility to establish environmental stewardship when they globally expand in terms of operations, supply chain and offerings. By prioritizing the initiatives retail chains can reduce impacts on environment, resource waster and mitigate risks related to that with the help of concepts like IoT. OBJECTIVES: This paper aims to explore how IoT can aid in sustainable practices, mitigate risks, and enhance efficiency while addressing challenges, ultimately providing insights for retail chains to prioritize sustainability in the IoT context. METHODS: The research employs a qualitative approach, focusing on in-depth case studies and analysis of industry reports and literature to explore IoT-driven sustainability initiatives in retail chains. It includes a diverse sample of retail chains, such as supermarkets and fashion retail, selected based on data availability related to their use of IoT for sustainability. The study involves descriptive analysis to present an overview of these initiatives and competitive analysis to identify sustainability leaders and areas for improvement. However, limitations include potential data availability issues and reliance on publicly available sources, with findings reflecting data up to the 2018-2021 timeframe. RESULTS: The results highlight significant sustainability benefits achieved through IoT integration in various retail chain types. Case studies, such as Sainsbury's and Coca-Cola, demonstrate waste reduction and sustainable practices. Examples from Nordstrom and 7-Eleven showcase energy efficiency improvements. The versatility of IoT technologies across supermarkets, department stores, and convenience stores emphasizes the transformative power of IoT in driving sustainability in the retail industry. The study proposes a prioritization approach, considering key metrics and leveraging frameworks like the Triple Bottom Line, Life Cycle Sustainability Assessment, and Sustainability Framework for effective decision-making and goal alignment in IoT-driven sustainability initiatives. CONCLUSION: In conclusion, this paper highlights the substantial potential of prioritizing IoT-driven sustainability initiatives in retail chains for positive environmental, social, and economic outcomes. Through case studies, the diverse applications of IoT, such as food waste reduction and energy-efficient lighting, demonstrate tangible benefits. The trend towards sustainable sourcing and materials is evident across various retail chain types. The discussion underscores the need for a systematic approach, utilizing frameworks like the Triple Bottom Line, to align with strategic objectives and optimize resources.
{"title":"Prioritizing IoT-driven Sustainability Initiatives in Retail Chains: Exploring Case Studies and Industry Insights","authors":"Krishnan Siva Karthikeyan, T. Nagaprakash","doi":"10.4108/eetiot.4628","DOIUrl":"https://doi.org/10.4108/eetiot.4628","url":null,"abstract":"INTRODUCTION: Prioritizing sustainability initiatives is crucial for retail chains as they integrate Internet of Things (IoT) technologies to drive environmental responsibility. Retail chains have responsibility to establish environmental stewardship when they globally expand in terms of operations, supply chain and offerings. By prioritizing the initiatives retail chains can reduce impacts on environment, resource waster and mitigate risks related to that with the help of concepts like IoT. OBJECTIVES: This paper aims to explore how IoT can aid in sustainable practices, mitigate risks, and enhance efficiency while addressing challenges, ultimately providing insights for retail chains to prioritize sustainability in the IoT context. METHODS: The research employs a qualitative approach, focusing on in-depth case studies and analysis of industry reports and literature to explore IoT-driven sustainability initiatives in retail chains. It includes a diverse sample of retail chains, such as supermarkets and fashion retail, selected based on data availability related to their use of IoT for sustainability. The study involves descriptive analysis to present an overview of these initiatives and competitive analysis to identify sustainability leaders and areas for improvement. However, limitations include potential data availability issues and reliance on publicly available sources, with findings reflecting data up to the 2018-2021 timeframe. RESULTS: The results highlight significant sustainability benefits achieved through IoT integration in various retail chain types. Case studies, such as Sainsbury's and Coca-Cola, demonstrate waste reduction and sustainable practices. Examples from Nordstrom and 7-Eleven showcase energy efficiency improvements. The versatility of IoT technologies across supermarkets, department stores, and convenience stores emphasizes the transformative power of IoT in driving sustainability in the retail industry. The study proposes a prioritization approach, considering key metrics and leveraging frameworks like the Triple Bottom Line, Life Cycle Sustainability Assessment, and Sustainability Framework for effective decision-making and goal alignment in IoT-driven sustainability initiatives. CONCLUSION: In conclusion, this paper highlights the substantial potential of prioritizing IoT-driven sustainability initiatives in retail chains for positive environmental, social, and economic outcomes. Through case studies, the diverse applications of IoT, such as food waste reduction and energy-efficient lighting, demonstrate tangible benefits. The trend towards sustainable sourcing and materials is evident across various retail chain types. The discussion underscores the need for a systematic approach, utilizing frameworks like the Triple Bottom Line, to align with strategic objectives and optimize resources.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"454 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139173202","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}
Abdoul Nasser Hamidou Soumana, M. ben Salah, S. Idbraim, A. Boulouz
The search for effective solutions to address traffic congestion presents a significant challenge for large urban cities. Analysis of urban traffic congestion has revealed that more than 70% of it can be attributed to prolonged searches for parking spaces. Consequently, accurate prediction of parking space availability in advance can play a vital role in assisting drivers to find vacant parking spaces quickly. Such solutions hold the potential to reduce traffic congestion and mitigate its detrimental impacts on the environment, economy, and public health. Machine learning algorithms have emerged as promising approaches for predicting parking space availability. However, comparative studies on those machine learning models to evaluate the best suited for a large-scale prediction and within a given prediction time period are missing.In this study, we compared nine machine learning algorithms to assess their efficiency in predicting long-term, large-scale parking space availability. Our comparison was based on two approaches: using on-street parking data alone and 2) incorporating data from external sources (such as weather data). We used automatic machine learning models to compare the performance of different algorithms according to the prediction efficiency and execution time. Our results indicated that the automated machine learning models implemented were well fitted to our data. Notably, the Extra Tree and Random Forest algorithms demonstrated the highest efficiency among the models tested. Moreover, we observed that the Random Forest algorithm exhibited less computational demand than the Extra Tree algorithm, making it particularly advantageous in terms of execution time. Therefore, this work suggests that the Random Forest algorithm is the most suitable machine learning model in terms of efficiency and execution time for accurately predicting large-scale, long-term parking space availability.
寻找解决交通拥堵问题的有效方案是大城市面临的一项重大挑战。对城市交通拥堵的分析表明，70%以上的交通拥堵可归因于长时间寻找停车位。因此，提前准确预测停车位的可用性对于帮助驾驶员快速找到空闲停车位至关重要。这种解决方案有可能减少交通拥堵，减轻其对环境、经济和公共健康的不利影响。机器学习算法已成为预测停车位可用性的有效方法。在本研究中，我们比较了九种机器学习算法，以评估它们在预测长期、大规模停车位可用性方面的效率。我们的比较基于两种方法：1）单独使用路边停车数据；2）结合外部数据源（如天气数据）。我们使用自动机器学习模型，根据预测效率和执行时间来比较不同算法的性能。结果表明，所实施的自动机器学习模型与我们的数据非常匹配。值得注意的是，在所测试的模型中， Extra Tree 算法和随机森林算法的效率最高。此外，我们还观察到，随机森林算法的计算需求低于额外树算法，因此在执行时间方面特别有优势。因此，这项工作表明，就效率和执行时间而言，随机森林算法是最适合用于准确预测大规模、长期停车位可用性的机器学习模型。
{"title":"Machine Learning Models in the large-scale prediction of parking space availability for sustainable cities","authors":"Abdoul Nasser Hamidou Soumana, M. ben Salah, S. Idbraim, A. Boulouz","doi":"10.4108/eetiot.2269","DOIUrl":"https://doi.org/10.4108/eetiot.2269","url":null,"abstract":"The search for effective solutions to address traffic congestion presents a significant challenge for large urban cities. Analysis of urban traffic congestion has revealed that more than 70% of it can be attributed to prolonged searches for parking spaces. Consequently, accurate prediction of parking space availability in advance can play a vital role in assisting drivers to find vacant parking spaces quickly. Such solutions hold the potential to reduce traffic congestion and mitigate its detrimental impacts on the environment, economy, and public health. Machine learning algorithms have emerged as promising approaches for predicting parking space availability. However, comparative studies on those machine learning models to evaluate the best suited for a large-scale prediction and within a given prediction time period are missing.In this study, we compared nine machine learning algorithms to assess their efficiency in predicting long-term, large-scale parking space availability. Our comparison was based on two approaches: using on-street parking data alone and 2) incorporating data from external sources (such as weather data). We used automatic machine learning models to compare the performance of different algorithms according to the prediction efficiency and execution time. Our results indicated that the automated machine learning models implemented were well fitted to our data. Notably, the Extra Tree and Random Forest algorithms demonstrated the highest efficiency among the models tested. Moreover, we observed that the Random Forest algorithm exhibited less computational demand than the Extra Tree algorithm, making it particularly advantageous in terms of execution time. Therefore, this work suggests that the Random Forest algorithm is the most suitable machine learning model in terms of efficiency and execution time for accurately predicting large-scale, long-term parking space availability.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"126 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139197250","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}
Over the past 20 years, there have been significant changes in the supply chain business. One of the most significant changes has been the development of supply chain management systems. It is now essential to use cutting-edge technologies to maintain competitiveness in a highly dynamic environment. Restocking inventories is one of a supplier’s main survival strategies and knowing what expenses to expect in the next month aids in better decision-making. This study aims to solve the three most common industry problems in Supply Chain – Inventory Management, Budget Fore-casting, and Cost vs Benefit of every supplier. The selection of the best forecasting model is still a major problem in much research in literature. In this context, this article aims to compare the performances of Auto-Regressive Integrated Moving Average (ARIMA), Holt-Winters (HW), and Long Short-Term Memory (LSTM) models for the prediction of a time series formed by the dataset of Supply Chain products. As performance measures, metric analysis of the Root Mean Square Error (RMSE) is used. The main concentration is on the Automotive Business Unit with the top 3 products under this segment and the country United States being in focus. All three models, ARIMA, HW, and LSTM obtained better results regarding the performance metrics.
{"title":"Demand Forecasting and Budget Planning for Automotive Supply Chain","authors":"Anand Limbare, Rashmi Agarwal","doi":"10.4108/eetiot.4514","DOIUrl":"https://doi.org/10.4108/eetiot.4514","url":null,"abstract":"Over the past 20 years, there have been significant changes in the supply chain business. One of the most significant changes has been the development of supply chain management systems. It is now essential to use cutting-edge technologies to maintain competitiveness in a highly dynamic environment. Restocking inventories is one of a supplier’s main survival strategies and knowing what expenses to expect in the next month aids in better decision-making. This study aims to solve the three most common industry problems in Supply Chain – Inventory Management, Budget Fore-casting, and Cost vs Benefit of every supplier. The selection of the best forecasting model is still a major problem in much research in literature. In this context, this article aims to compare the performances of Auto-Regressive Integrated Moving Average (ARIMA), Holt-Winters (HW), and Long Short-Term Memory (LSTM) models for the prediction of a time series formed by the dataset of Supply Chain products. As performance measures, metric analysis of the Root Mean Square Error (RMSE) is used. The main concentration is on the Automotive Business Unit with the top 3 products under this segment and the country United States being in focus. All three models, ARIMA, HW, and LSTM obtained better results regarding the performance metrics.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139207280","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}
A wireless network is a collection of computers and other electronic devices that exchange information by means of radio waves. Endpoint computing devices can all be connected without the need for hardwired data cabling thanks to the prevalence of wireless networks in today's businesses and networks. This paper's aim is to create and construct a wireless network model for connecting two hosts which will be implemented to simulate wireless communications. The sending of User Datagram Protocol (UPD) data by one of the hosts to the other one has been wirelessly specified by the simulator. Additionally, the protocol models were kept as simple as possible including both the physical layer and the lower layer. The architecture and functionality of a new simulator is showed its ability to solve the issues of making a host move, especially, when it gets out of the range the simulation ends.
{"title":"Development and Simulation Two Wireless Hosts Communication Network Using Omnnet++","authors":"M. Derbali","doi":"10.4108/eetiot.4519","DOIUrl":"https://doi.org/10.4108/eetiot.4519","url":null,"abstract":"A wireless network is a collection of computers and other electronic devices that exchange information by means of radio waves. Endpoint computing devices can all be connected without the need for hardwired data cabling thanks to the prevalence of wireless networks in today's businesses and networks. This paper's aim is to create and construct a wireless network model for connecting two hosts which will be implemented to simulate wireless communications. The sending of User Datagram Protocol (UPD) data by one of the hosts to the other one has been wirelessly specified by the simulator. Additionally, the protocol models were kept as simple as possible including both the physical layer and the lower layer. The architecture and functionality of a new simulator is showed its ability to solve the issues of making a host move, especially, when it gets out of the range the simulation ends.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139202437","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}
Henil Gajjar, Trushti Selarka, A. Lakdawala, Dhaval B. Shah, P. N. Kapil
Excessive noise in speech communication systems is a major issue affecting various fields, including teleconferencing and hearing aid systems. To tackle this issue, various deep-learning models have been proposed, with autoencoder-based models showing remarkable results. In this paper, we present a comparative analysis of four different deep learning based autoencoder models, namely model ‘alpha’, model ‘beta’, model ‘gamma’, and model ‘delta’ for noise suppression in speech signals. The performance of each model was evaluated using objective metric, mean squared error (MSE). Our experimental results showed that the model ‘alpha’ outperformed the other models, achieving a minimum error of 0.0086 and maximum error of 0.0158. The model ‘gamma’ also performed well, with a minimum error of 0.0169 and maximum error of 0.0216. These findings suggest that the pro-posed models have great potential for enhancing speech communication systems in various fields.
{"title":"A Comparative Analysis of Various Deep-Learning Models for Noise Suppression","authors":"Henil Gajjar, Trushti Selarka, A. Lakdawala, Dhaval B. Shah, P. N. Kapil","doi":"10.4108/eetiot.4502","DOIUrl":"https://doi.org/10.4108/eetiot.4502","url":null,"abstract":"Excessive noise in speech communication systems is a major issue affecting various fields, including teleconferencing and hearing aid systems. To tackle this issue, various deep-learning models have been proposed, with autoencoder-based models showing remarkable results. In this paper, we present a comparative analysis of four different deep learning based autoencoder models, namely model ‘alpha’, model ‘beta’, model ‘gamma’, and model ‘delta’ for noise suppression in speech signals. The performance of each model was evaluated using objective metric, mean squared error (MSE). Our experimental results showed that the model ‘alpha’ outperformed the other models, achieving a minimum error of 0.0086 and maximum error of 0.0158. The model ‘gamma’ also performed well, with a minimum error of 0.0169 and maximum error of 0.0216. These findings suggest that the pro-posed models have great potential for enhancing speech communication systems in various fields.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139211118","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}
Milk is the main dietary supply for every individual. High-quality milk shouldn't contain any adulterants. Dairy products are sold everywhere in society. Yet, the local milk vendors use a wide range of adulterants in their products, permanently altering the evaporated. Using milk that has gone bad can have serious health consequences. On October 18 of this year, the Food Safety and Standards Authority of India (FSSAI), the nation's top food safety authority, released the final result of the National Milk Safety and Quality Survey (NMSQS) and declared the milk readily available in India to be "mostly safe." According to an FSSAI survey, 68.4% of the milk in India is tainted. The quality of milk cannot be checked by any equipment or special system. Milk that has not been pasteurized has not been treated to get rid of harmful bacteria. Infected raw milk may contain Salmonella, Campylobacter, Cryptosporidium, E. coli, Listeria, Brucella, and other dangerous pathogens. These microorganisms pose a major risk to your family's health. Manually analyzing the various milk constituents can be very challenging when determining the quality of the milk. Analyzing and discovering with the aid of machine learning can help with this endeavor. Here a machine learning-based milk quality prediction system is developed. The proposed technology has shown 99.99% classification accuracy.
{"title":"Milk Quality Prediction Using Machine Learning","authors":"Drashti Bhavsar, Yash Jobanputra, Nirmal Keshari Swain, Debabrata Swain","doi":"10.4108/eetiot.4501","DOIUrl":"https://doi.org/10.4108/eetiot.4501","url":null,"abstract":"Milk is the main dietary supply for every individual. High-quality milk shouldn't contain any adulterants. Dairy products are sold everywhere in society. Yet, the local milk vendors use a wide range of adulterants in their products, permanently altering the evaporated. Using milk that has gone bad can have serious health consequences. On October 18 of this year, the Food Safety and Standards Authority of India (FSSAI), the nation's top food safety authority, released the final result of the National Milk Safety and Quality Survey (NMSQS) and declared the milk readily available in India to be \"mostly safe.\" According to an FSSAI survey, 68.4% of the milk in India is tainted. The quality of milk cannot be checked by any equipment or special system. Milk that has not been pasteurized has not been treated to get rid of harmful bacteria. Infected raw milk may contain Salmonella, Campylobacter, Cryptosporidium, E. coli, Listeria, Brucella, and other dangerous pathogens. These microorganisms pose a major risk to your family's health. Manually analyzing the various milk constituents can be very challenging when determining the quality of the milk. Analyzing and discovering with the aid of machine learning can help with this endeavor. Here a machine learning-based milk quality prediction system is developed. The proposed technology has shown 99.99% classification accuracy.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139211047","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}
INTRODUCTION: The division of a 3D point cloud into various meaningful regions or objects is known as point cloud segmentation. OBJECTIVES: The paper discusses the challenges faced in 3D point cloud segmentation, such as the high dimensionality of point cloud data, noise, and varying point densities. METHODS: The paper compares several commonly used datasets in the field, including the ModelNet, ScanNet, S3DIS, and Semantic 3D datasets, ApploloCar3D, and provides an analysis of the strengths and weaknesses of each dataset. Also provides an overview of the papers that uses Traditional clustering techniques, deep learning-based methods, and hybrid approaches in point cloud semantic segmentation. The report also discusses the benefits and drawbacks of each approach. CONCLUSION: This study sheds light on the state of the art in semantic segmentation of 3D point clouds.
{"title":"Analysis of Current Advancement in 3D Point Cloud Semantic Segmentation","authors":"Koneru Pranav Sai, Sagar Dhanaraj Pande","doi":"10.4108/eetiot.4495","DOIUrl":"https://doi.org/10.4108/eetiot.4495","url":null,"abstract":"INTRODUCTION: The division of a 3D point cloud into various meaningful regions or objects is known as point cloud segmentation. OBJECTIVES: The paper discusses the challenges faced in 3D point cloud segmentation, such as the high dimensionality of point cloud data, noise, and varying point densities. METHODS: The paper compares several commonly used datasets in the field, including the ModelNet, ScanNet, S3DIS, and Semantic 3D datasets, ApploloCar3D, and provides an analysis of the strengths and weaknesses of each dataset. Also provides an overview of the papers that uses Traditional clustering techniques, deep learning-based methods, and hybrid approaches in point cloud semantic segmentation. The report also discusses the benefits and drawbacks of each approach. CONCLUSION: This study sheds light on the state of the art in semantic segmentation of 3D point clouds.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139219597","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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