Data imputation of missing values is one of the critical issues for data engineering, such as air quality modeling. It is challenging to handle missing pollutant values because they are collected at irregular and different times. Accurate estimation of those missing values is critical for the air pollution prediction task. Effective forecasting is a significant part of air quality modeling for a robust early warning system. This study developed a neural network model, a Temporal Convolutional Network (TCN) with an imputation block (TCN-I), to simultaneously perform data imputation and forecasting tasks. As pollution sensor data suffer from different types of missing values whose causes are varied, TCN is attempted to impute those missing values in this study and perform prediction tasks in a single model. The results prove that the TCN-I model outperforms the baseline models.
{"title":"Auto imputation enabled deep Temporal Convolutional Network (TCN) model for pm2.5 forecasting","authors":"K. Krishna, Rani Samal","doi":"10.4108/eetsis.5102","DOIUrl":"https://doi.org/10.4108/eetsis.5102","url":null,"abstract":"Data imputation of missing values is one of the critical issues for data engineering, such as air quality modeling. It is challenging to handle missing pollutant values because they are collected at irregular and different times. Accurate estimation of those missing values is critical for the air pollution prediction task. Effective forecasting is a significant part of air quality modeling for a robust early warning system. This study developed a neural network model, a Temporal Convolutional Network (TCN) with an imputation block (TCN-I), to simultaneously perform data imputation and forecasting tasks. As pollution sensor data suffer from different types of missing values whose causes are varied, TCN is attempted to impute those missing values in this study and perform prediction tasks in a single model. The results prove that the TCN-I model outperforms the baseline models.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"88 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141657663","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}
Lizet Doriela Mantari Mincami, Hilario Romero Giron, Edith Mariela Quispe Sanabria, Luis Alberto Poma Lago, Jose Francisco Via y Rada Vittes, Jessenia Vasquez Artica, Linda Flor Villa Ricapa
Introduction: Citizen science has generated large volumes of data contributed by citizens in the last decade. However, the lack of standardization in metadata threatens the interoperability and reuse of information.Objective: The objective was to develop a proposal for standards to document metadata in citizen science projects in order to improve interoperability and data reuse.Methods: A literature review was conducted that characterized the challenges in metadata documentation. Likewise, it analyzed previous experiences with standards such as Darwin Core and Dublin Core.Results: The review showed a high heterogeneity in the documentation, making interoperability difficult. The analyzes showed that standards facilitate the flow of information when they cover basic needs.Conclusions: It was concluded that standardizing metadata is essential to harness the potential of citizen science. The initial proposal, consisting of flexible norms focused on critical aspects, sought to establish bases for a collaborative debate considering the changing needs of this community.
{"title":"Development of Standards for Metadata Documentation in Citizen Science Projects","authors":"Lizet Doriela Mantari Mincami, Hilario Romero Giron, Edith Mariela Quispe Sanabria, Luis Alberto Poma Lago, Jose Francisco Via y Rada Vittes, Jessenia Vasquez Artica, Linda Flor Villa Ricapa","doi":"10.4108/eetsis.5704","DOIUrl":"https://doi.org/10.4108/eetsis.5704","url":null,"abstract":"Introduction: Citizen science has generated large volumes of data contributed by citizens in the last decade. However, the lack of standardization in metadata threatens the interoperability and reuse of information.Objective: The objective was to develop a proposal for standards to document metadata in citizen science projects in order to improve interoperability and data reuse.Methods: A literature review was conducted that characterized the challenges in metadata documentation. Likewise, it analyzed previous experiences with standards such as Darwin Core and Dublin Core.Results: The review showed a high heterogeneity in the documentation, making interoperability difficult. The analyzes showed that standards facilitate the flow of information when they cover basic needs.Conclusions: It was concluded that standardizing metadata is essential to harness the potential of citizen science. The initial proposal, consisting of flexible norms focused on critical aspects, sought to establish bases for a collaborative debate considering the changing needs of this community.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"18 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140660460","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}
Viraj Nishchal Shah, Deep Rahul Shah, M. Shetty, Deepa Krishnan, Vinayakumar Ravi, Swapnil Singh
INTRODUCTION: Language serves as the primary conduit for human expression, extending its reach into various communication mediums like email and text messaging, where emoticons are frequently employed to convey nuanced emotions. In the digital landscape of long-distance communication, the detection and analysis of emotions assume paramount importance. However, this task is inherently challenging due to the subjectivity inherent in emotions, lacking a universal consensus for quantification or categorization.OBJECTIVES: This research proposes a novel speech recognition model for emotion analysis, leveraging diverse machine learning techniques along with a three-layer feature extraction approach. This research will also through light on the robustness of models on balanced and imbalanced datasets. METHODS: The proposed three-layered feature extractor uses chroma, MFCC, and Mel method, and passes these features to classifiers like K-Nearest Neighbour, Gradient Boosting, Multi-Layer Perceptron, and Random Forest.RESULTS: Among the classifiers in the framework, Multi-Layer Perceptron (MLP) emerges as the top-performing model, showcasing remarkable accuracies of 99.64%, 99.43%, and 99.31% in the Balanced TESS Dataset, Imbalanced TESS (Half) Dataset, and Imbalanced TESS (Quarter) Dataset, respectively. K-Nearest Neighbour (KNN) follows closely as the second-best classifier, surpassing MLP's accuracy only in the Imbalanced TESS (Half) Dataset at 99.52%.CONCLUSION: This research contributes valuable insights into effective emotion recognition through speech, shedding light on the nuances of classification in imbalanced datasets.
{"title":"Investigation of Imbalanced Sentiment Analysis in Voice Data: A Comparative Study of Machine Learning Algorithms","authors":"Viraj Nishchal Shah, Deep Rahul Shah, M. Shetty, Deepa Krishnan, Vinayakumar Ravi, Swapnil Singh","doi":"10.4108/eetsis.4805","DOIUrl":"https://doi.org/10.4108/eetsis.4805","url":null,"abstract":" INTRODUCTION: Language serves as the primary conduit for human expression, extending its reach into various communication mediums like email and text messaging, where emoticons are frequently employed to convey nuanced emotions. In the digital landscape of long-distance communication, the detection and analysis of emotions assume paramount importance. However, this task is inherently challenging due to the subjectivity inherent in emotions, lacking a universal consensus for quantification or categorization.OBJECTIVES: This research proposes a novel speech recognition model for emotion analysis, leveraging diverse machine learning techniques along with a three-layer feature extraction approach. This research will also through light on the robustness of models on balanced and imbalanced datasets. METHODS: The proposed three-layered feature extractor uses chroma, MFCC, and Mel method, and passes these features to classifiers like K-Nearest Neighbour, Gradient Boosting, Multi-Layer Perceptron, and Random Forest.RESULTS: Among the classifiers in the framework, Multi-Layer Perceptron (MLP) emerges as the top-performing model, showcasing remarkable accuracies of 99.64%, 99.43%, and 99.31% in the Balanced TESS Dataset, Imbalanced TESS (Half) Dataset, and Imbalanced TESS (Quarter) Dataset, respectively. K-Nearest Neighbour (KNN) follows closely as the second-best classifier, surpassing MLP's accuracy only in the Imbalanced TESS (Half) Dataset at 99.52%.CONCLUSION: This research contributes valuable insights into effective emotion recognition through speech, shedding light on the nuances of classification in imbalanced datasets.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"19 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140673393","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 major step forward in educational technology is the application of Data Science additionally Artificial Intelligence (AI) into undergraduate English courses. Improving teaching approaches and student involvement in the context of English language acquisition is an important issue that this study seeks to address. Even though there have been great strides in educational technology, conventional English classes still have a hard time meeting the demands of their different student bodies and offering individualized lessons. This is a major problem that prevents English language training from being effective, according to the material that is already available. In this study, we provide an approach to this issue called English Smart Classroom Teaching with the Internet of Things (ESCT-IoT). Utilizing data science techniques, artificial intelligence (AI) algorithms, and Internet of Things (IoT) sensors, ESCT-IoT intends to provide a personalized learning environment that is both immersive and adaptable. The fuzzy hierarchical evaluation technique is used to determine the assessment's final result, which measures the smart classroom's instructional impact. To overcome the limitations of conventional education, ESCT-IoT gathers and analyses data in real time to give adaptive material, individualized feedback, and learning suggestions. There are noticeable benefits as compared to traditional methods of instruction when it comes to evaluation metrics like student engagement, learning outcomes, and teacher satisfaction. Furthermore, ESCT-IoT is excellent in encouraging active learning, improving language fluency, and boosting overall academic achievement, according to qualitative comments from both students and teachers.
{"title":"The Digital Transformation of College English Classroom: Application of Artificial Intelligence and Data Science","authors":"Yanling Li","doi":"10.4108/eetsis.5636","DOIUrl":"https://doi.org/10.4108/eetsis.5636","url":null,"abstract":"A major step forward in educational technology is the application of Data Science additionally Artificial Intelligence (AI) into undergraduate English courses. Improving teaching approaches and student involvement in the context of English language acquisition is an important issue that this study seeks to address. Even though there have been great strides in educational technology, conventional English classes still have a hard time meeting the demands of their different student bodies and offering individualized lessons. This is a major problem that prevents English language training from being effective, according to the material that is already available. In this study, we provide an approach to this issue called English Smart Classroom Teaching with the Internet of Things (ESCT-IoT). Utilizing data science techniques, artificial intelligence (AI) algorithms, and Internet of Things (IoT) sensors, ESCT-IoT intends to provide a personalized learning environment that is both immersive and adaptable. The fuzzy hierarchical evaluation technique is used to determine the assessment's final result, which measures the smart classroom's instructional impact. To overcome the limitations of conventional education, ESCT-IoT gathers and analyses data in real time to give adaptive material, individualized feedback, and learning suggestions. There are noticeable benefits as compared to traditional methods of instruction when it comes to evaluation metrics like student engagement, learning outcomes, and teacher satisfaction. Furthermore, ESCT-IoT is excellent in encouraging active learning, improving language fluency, and boosting overall academic achievement, according to qualitative comments from both students and teachers.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"1988 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140718965","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 access gateway layer in the IoT interior design bridging the gap between several destinations. The capabilities include message routing, message identification, and a service. IoT intelligence can help machinery industries optimize their operations with perspectives on factory processes, energy use, and help efficiency. Automation can bring in improved operations, lower destruction, and greater manufacture. IoT barriers are exactly developed for bridging the gap between field devices and focused revenues and industrial applications, maximizing intelligent system performance and receiving and processing real-time operational control data that the network edge. The creation of powerful, flexible, and adjustable Human Machine Interfaces (HMI) can enable associates with information and tailored solutions to increase productivity while remaining safe. An innovative strategy for data-enabled engineering advances based on the Internet of Manufacturing Things (IoMT) is essential for effectively utilizing physical mechanisms. The proposed method HMI-IoMT has been gap analysis to other business processes turns into a reporting process that can be utilized for improvement. Implementing a gap analysis in production or manufacturing can bring the existing level of manpower allocation closer to an ideal level due to balancing and integrating the resources. Societal growth and connection are both aided in the built environment. Manufacturing operations are made much more productive with the help of automation and advanced machinery. Increasing the output of products and services is possible as a result of this efficiency, which allows for the fulfillment of an expanding population's necessities.
{"title":"Intelligent manufacturing: bridging the gap between the Internet of Things and machinery to achieve optimized operations","authors":"Yuanfang Wei, Li Song","doi":"10.4108/eetsis.5671","DOIUrl":"https://doi.org/10.4108/eetsis.5671","url":null,"abstract":"The access gateway layer in the IoT interior design bridging the gap between several destinations. The capabilities include message routing, message identification, and a service. IoT intelligence can help machinery industries optimize their operations with perspectives on factory processes, energy use, and help efficiency. Automation can bring in improved operations, lower destruction, and greater manufacture. IoT barriers are exactly developed for bridging the gap between field devices and focused revenues and industrial applications, maximizing intelligent system performance and receiving and processing real-time operational control data that the network edge. The creation of powerful, flexible, and adjustable Human Machine Interfaces (HMI) can enable associates with information and tailored solutions to increase productivity while remaining safe. An innovative strategy for data-enabled engineering advances based on the Internet of Manufacturing Things (IoMT) is essential for effectively utilizing physical mechanisms. The proposed method HMI-IoMT has been gap analysis to other business processes turns into a reporting process that can be utilized for improvement. Implementing a gap analysis in production or manufacturing can bring the existing level of manpower allocation closer to an ideal level due to balancing and integrating the resources. Societal growth and connection are both aided in the built environment. Manufacturing operations are made much more productive with the help of automation and advanced machinery. Increasing the output of products and services is possible as a result of this efficiency, which allows for the fulfillment of an expanding population's necessities.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"258 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140719852","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 the realm of Unmanned Aerial Vehicles (UAVs) for civilian applications, the surge in demand has underscored the need for sophisticated technologies. The integration of Unmanned Aerial Systems (UAS) with Artificial Intelligence (AI) has become paramount to address challenges in urban environments, particularly those involving obstacle collision risks. These UAVs are equipped with advanced sensor arrays, incorporating LiDAR and computer vision technologies. The AI algorithm undergoes comprehensive training on an embedded machine, fostering the development of a robust spatial perception model. This model enables the UAV to interpret and navigate through the intricate urban landscape with a human-like understanding of its surroundings. During mission execution, the AI-driven perception system detects and localizes objects, ensuring real-time awareness. This study proposes an innovative real-time three-dimensional (3D) path planner designed to optimize UAV trajectories through obstacle-laden environments. The path planner leverages a heuristic A* algorithm, a widely recognized search algorithm in artificial intelligence. A distinguishing feature of this proposed path planner is its ability to operate without the need to store frontier nodes in memory, diverging from conventional A* implementations. Instead, it relies on relative object positions obtained from the perception system, employing advanced techniques in simultaneous localization and mapping (SLAM). This approach ensures the generation of collision-free paths, enhancing the UAV's navigational efficiency. Moreover, the proposed path planner undergoes rigorous validation through Software-In-The-Loop (SITL) simulations in constrained environments, leveraging high-fidelity UAV dynamics models. Preliminary real flight tests are conducted to assess the real-world applicability of the system, considering factors such as wind disturbances and dynamic obstacles. The results showcase the path planner's effectiveness in providing swift and accurate guidance, thereby establishing its viability for real-time UAV missions in complex urban scenarios.
{"title":"Real-Time 3D Routing Optimization for Unmanned Aerial Vehicle using Machine Learning","authors":"Priya Mishra, Balaji Boopal, Naveen Mishra","doi":"10.4108/eetsis.5693","DOIUrl":"https://doi.org/10.4108/eetsis.5693","url":null,"abstract":"In the realm of Unmanned Aerial Vehicles (UAVs) for civilian applications, the surge in demand has underscored the need for sophisticated technologies. The integration of Unmanned Aerial Systems (UAS) with Artificial Intelligence (AI) has become paramount to address challenges in urban environments, particularly those involving obstacle collision risks. These UAVs are equipped with advanced sensor arrays, incorporating LiDAR and computer vision technologies. The AI algorithm undergoes comprehensive training on an embedded machine, fostering the development of a robust spatial perception model. This model enables the UAV to interpret and navigate through the intricate urban landscape with a human-like understanding of its surroundings. During mission execution, the AI-driven perception system detects and localizes objects, ensuring real-time awareness. This study proposes an innovative real-time three-dimensional (3D) path planner designed to optimize UAV trajectories through obstacle-laden environments. The path planner leverages a heuristic A* algorithm, a widely recognized search algorithm in artificial intelligence. A distinguishing feature of this proposed path planner is its ability to operate without the need to store frontier nodes in memory, diverging from conventional A* implementations. Instead, it relies on relative object positions obtained from the perception system, employing advanced techniques in simultaneous localization and mapping (SLAM). This approach ensures the generation of collision-free paths, enhancing the UAV's navigational efficiency. Moreover, the proposed path planner undergoes rigorous validation through Software-In-The-Loop (SITL) simulations in constrained environments, leveraging high-fidelity UAV dynamics models. Preliminary real flight tests are conducted to assess the real-world applicability of the system, considering factors such as wind disturbances and dynamic obstacles. The results showcase the path planner's effectiveness in providing swift and accurate guidance, thereby establishing its viability for real-time UAV missions in complex urban scenarios.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"111 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140724520","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}
Sentiment analysis, a critical task in natural language processing, aims to automatically identify and classify the sentiment expressed in textual data. Aspect-level sentiment analysis focuses on determining sentiment at a more granular level, targeting specific aspects or features within a piece of text. In this paper, we explore various techniques for sentiment analysis, including traditional machine learning approaches and state-of-the-art deep learning models. Additionally, deep learning techniques has been utilized to identifying and extracting specific aspects from text, addressing aspect-level ambiguity, and capturing nuanced sentiments for each aspect. These datasets are valuable for conducting aspect-level sentiment analysis. In this article, we explore a language model based on pre-trained deep neural networks. This model can analyze sequences of text to classify sentiments as positive, negative, or neutral without explicit human labeling. To evaluate these models, data from Twitter's US airlines sentiment database was utilized. Experiments on this dataset reveal that the BERT, RoBERTA and DistilBERT model outperforms than the ML based model in accuracy and is more efficient in terms of training time. Notably, our findings showcase significant advancements over previous state-of-the-art methods that rely on supervised feature learning, bridging existing gaps in sentiment analysis methodologies. Our findings shed light on the advancements and challenges in sentiment analysis, offering insights for future research directions and practical applications in areas such as customer feedback analysis, social media monitoring, and opinion mining.
情感分析是自然语言处理中的一项重要任务，旨在自动识别文本数据中表达的情感并对其进行分类。方面级情感分析侧重于在更细的层面上确定情感，针对的是文本中的特定方面或特征。本文探讨了情感分析的各种技术，包括传统的机器学习方法和最先进的深度学习模型。此外，我们还利用深度学习技术来识别和提取文本中的特定方面，解决方面层面的模糊性问题，并捕捉每个方面的细微情感。这些数据集对于进行方面级情感分析非常有价值。在本文中，我们将探讨一种基于预训练深度神经网络的语言模型。该模型可以分析文本序列，将情感分类为正面、负面或中性，而无需明确的人工标注。为了评估这些模型，我们使用了 Twitter 美国航空公司情感数据库中的数据。在该数据集上进行的实验表明，BERT、RoBERTA 和 DistilBERT 模型在准确性上优于基于 ML 的模型，而且在训练时间上更有效。值得注意的是，我们的研究结果表明，与以前依赖于监督特征学习的最先进方法相比，我们的研究取得了重大进步，弥补了情感分析方法中的现有差距。我们的研究结果揭示了情感分析的进步与挑战，为未来的研究方向以及客户反馈分析、社交媒体监测和意见挖掘等领域的实际应用提供了启示。
{"title":"Manifesto of Deep Learning Architecture for Aspect Level Sentiment Analysis to extract customer criticism","authors":"N. Kushwaha, B. Singh, S. Agrawal","doi":"10.4108/eetsis.5698","DOIUrl":"https://doi.org/10.4108/eetsis.5698","url":null,"abstract":"Sentiment analysis, a critical task in natural language processing, aims to automatically identify and classify the sentiment expressed in textual data. Aspect-level sentiment analysis focuses on determining sentiment at a more granular level, targeting specific aspects or features within a piece of text. In this paper, we explore various techniques for sentiment analysis, including traditional machine learning approaches and state-of-the-art deep learning models. Additionally, deep learning techniques has been utilized to identifying and extracting specific aspects from text, addressing aspect-level ambiguity, and capturing nuanced sentiments for each aspect. These datasets are valuable for conducting aspect-level sentiment analysis. In this article, we explore a language model based on pre-trained deep neural networks. This model can analyze sequences of text to classify sentiments as positive, negative, or neutral without explicit human labeling. To evaluate these models, data from Twitter's US airlines sentiment database was utilized. Experiments on this dataset reveal that the BERT, RoBERTA and DistilBERT model outperforms than the ML based model in accuracy and is more efficient in terms of training time. Notably, our findings showcase significant advancements over previous state-of-the-art methods that rely on supervised feature learning, bridging existing gaps in sentiment analysis methodologies. Our findings shed light on the advancements and challenges in sentiment analysis, offering insights for future research directions and practical applications in areas such as customer feedback analysis, social media monitoring, and opinion mining.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"49 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140726228","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: In an effort to enhance the quality of user experience in using music services and improve the efficiency of music recommendation platforms, researching accurate and efficient music recommendation methods and constructing an accurate real-time online recommendation platform are the key points for the success of a high-quality music website platform.OBJECTIVES: To address the problems of incomplete signal feature capture, insufficient classification efficiency and poor generalization of current music recommendation methods.METHODS: Improve the deep confidence network to construct music recommendation algorithm by using big data and intelligent optimization algorithm. Firstly, music features are extracted by analyzing the principle of music recommendation algorithm, and evaluation indexes of music recommendation algorithm are proposed at the same time; then, combined with the deep sleep optimization algorithm, a music recommendation method based on improved deep confidence network is proposed; finally, the efficiency of the proposed method is verified through the analysis of simulation experiments.RESULTS: While meeting the real-time requirements, the proposed method improves the music recommendation accuracy, recall, and coverage.CONCLUSION: Solves the questions of incomplete signal feature capture, insufficient classification efficiency, and poor generalization of current music recommendation algorithms.
{"title":"An Improved Intelligent Machine Learning Approach to Music Recommendation Based on Big Data Techniques and DSO Algorithms","authors":"Sujie He, Yuxian Li","doi":"10.4108/eetsis.5176","DOIUrl":"https://doi.org/10.4108/eetsis.5176","url":null,"abstract":"INTRODUCTION: In an effort to enhance the quality of user experience in using music services and improve the efficiency of music recommendation platforms, researching accurate and efficient music recommendation methods and constructing an accurate real-time online recommendation platform are the key points for the success of a high-quality music website platform.OBJECTIVES: To address the problems of incomplete signal feature capture, insufficient classification efficiency and poor generalization of current music recommendation methods.METHODS: Improve the deep confidence network to construct music recommendation algorithm by using big data and intelligent optimization algorithm. Firstly, music features are extracted by analyzing the principle of music recommendation algorithm, and evaluation indexes of music recommendation algorithm are proposed at the same time; then, combined with the deep sleep optimization algorithm, a music recommendation method based on improved deep confidence network is proposed; finally, the efficiency of the proposed method is verified through the analysis of simulation experiments.RESULTS: While meeting the real-time requirements, the proposed method improves the music recommendation accuracy, recall, and coverage.CONCLUSION: Solves the questions of incomplete signal feature capture, insufficient classification efficiency, and poor generalization of current music recommendation algorithms.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"179 S446","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140731076","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: A lightweght lung image segmentation model was explored. It was with fast speed and low resouces consumed while the accuracy was comparable to those SOAT models. OBJECTIVES: To improve the segmentation accuracy and computational efficiency of the model in extracting lung regions from chest X-ray images, a lightweight segmentation model enhanced with a visual attention mechanism called VAEL-Unet, was proposed. METHODS: Firstly, the bneck module from the MobileNetV3 network was employed to replace the convolutional and pooling operations at different positions in the U-Net encoder, enabling the model to extract deeper-level features while reducing complexity and parameters. Secondly, an attention module was introduced during feature fusion, where the processed feature maps were sequentially fused with the corresponding positions in the decoder to obtain the segmented image. RESULTS: On ChestXray, the accuracy of VAEL-Unet improves from 97.37% in the traditional U-Net network to 97.69%, while the F1-score increases by 0.67%, 0.77%, 0.61%, and 1.03% compared to U-Net, SegNet, ResUnet and DeepLabV3+ networks. respectively. On LUNA dataset. the F1-score demonstrates improvements of 0.51%, 0.48%, 0.22% and 0.46%, respectively, while the accuracy has increased from 97.78% in the traditional U-Net model to 98.08% in the VAEL-Unet model. The training time of the VAEL-Unet is much less compared to other models. The number of parameters of VAEL-Unet is only 1.1M, significantly less than 32M of U-Net, 29M of SegNet, 48M of Res-Unet, 5.8M of DeeplabV3+ and 41M of DeepLabV3Plus_ResNet50. CONCLUSION: These results indicate that VAEL-Unet’s segmentation performance is slightly better than other referenced models while its training time and parameters are much less.
{"title":"Fast Lung Image Segmentation Using Lightweight VAEL-Unet","authors":"Xiulan Hao, Chuanjin Zhang, Shiluo Xu","doi":"10.4108/eetsis.4788","DOIUrl":"https://doi.org/10.4108/eetsis.4788","url":null,"abstract":"INTRODUCTION: A lightweght lung image segmentation model was explored. It was with fast speed and low resouces consumed while the accuracy was comparable to those SOAT models.\u0000OBJECTIVES: To improve the segmentation accuracy and computational efficiency of the model in extracting lung regions from chest X-ray images, a lightweight segmentation model enhanced with a visual attention mechanism called VAEL-Unet, was proposed.\u0000METHODS: Firstly, the bneck module from the MobileNetV3 network was employed to replace the convolutional and pooling operations at different positions in the U-Net encoder, enabling the model to extract deeper-level features while reducing complexity and parameters. Secondly, an attention module was introduced during feature fusion, where the processed feature maps were sequentially fused with the corresponding positions in the decoder to obtain the segmented image.\u0000RESULTS: On ChestXray, the accuracy of VAEL-Unet improves from 97.37% in the traditional U-Net network to 97.69%, while the F1-score increases by 0.67%, 0.77%, 0.61%, and 1.03% compared to U-Net, SegNet, ResUnet and DeepLabV3+ networks. respectively. On LUNA dataset. the F1-score demonstrates improvements of 0.51%, 0.48%, 0.22% and 0.46%, respectively, while the accuracy has increased from 97.78% in the traditional U-Net model to 98.08% in the VAEL-Unet model. The training time of the VAEL-Unet is much less compared to other models. The number of parameters of VAEL-Unet is only 1.1M, significantly less than 32M of U-Net, 29M of SegNet, 48M of Res-Unet, 5.8M of DeeplabV3+ and 41M of DeepLabV3Plus_ResNet50. \u0000CONCLUSION: These results indicate that VAEL-Unet’s segmentation performance is slightly better than other referenced models while its training time and parameters are much less.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"14 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140730097","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 research on the method of cultivating college students' autonomous ability based on experiential teaching is conducive to college students' change of learning mode and learning thinking, improving the utilisation rate of educational resources, as well as the reform of education.OBJECTIVES: Addressing the current problems of unquantified analyses, lack of breadth, and insufficient development strategies in the methods used to develop independent learning skills in university students.METHODS: This paper proposes an intelligent optimisation algorithm for the cultivation of college students' independent learning ability in experiential teaching. Firstly, the characteristics and elements of college students' independent learning are analysed, while the strategy of cultivating college students' independent learning ability in experiential teaching is proposed; then, the weight optimization method of cultivating college students' independent learning ability based on experiential teaching is proposed by using the improved intelligent optimization algorithm; finally, the validity and feasibility of the proposed method are verified through experimental analysis.RESULTS: The results show that the proposed method has a wider range of culture effects.CONCLUSION: Addressing the problem of poor generalisation in the development of independent learning skills among university students.
{"title":"A Self-learning Ability Assessment Method Based on Weight-Optimised Dfferential Evolutionary Algorithm","authors":"Zhiwei Zhu","doi":"10.4108/eetsis.5175","DOIUrl":"https://doi.org/10.4108/eetsis.5175","url":null,"abstract":"INTRODUCTION: The research on the method of cultivating college students' autonomous ability based on experiential teaching is conducive to college students' change of learning mode and learning thinking, improving the utilisation rate of educational resources, as well as the reform of education.OBJECTIVES: Addressing the current problems of unquantified analyses, lack of breadth, and insufficient development strategies in the methods used to develop independent learning skills in university students.METHODS: This paper proposes an intelligent optimisation algorithm for the cultivation of college students' independent learning ability in experiential teaching. Firstly, the characteristics and elements of college students' independent learning are analysed, while the strategy of cultivating college students' independent learning ability in experiential teaching is proposed; then, the weight optimization method of cultivating college students' independent learning ability based on experiential teaching is proposed by using the improved intelligent optimization algorithm; finally, the validity and feasibility of the proposed method are verified through experimental analysis.RESULTS: The results show that the proposed method has a wider range of culture effects.CONCLUSION: Addressing the problem of poor generalisation in the development of independent learning skills among university students.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"11 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140728610","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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