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}
Hameed Khan, Kamal K. Kushwah, Jitendra S Thakur, G. Soni, Abhishek Tripathi
Mobile Ad Hoc Networks (MANETs) pose a dynamically organized wireless network, posing a challenge to establishing quality of service (QoS) due to limitations in bandwidth and the ever-changing network topology. These networks are created by assembling nodes systematically, lacking a central infrastructure, and dynamically linking devices such as mobile phones and tablets. Nodes employ diverse methods for service delivery, all while giving priority to network performance. The effectiveness of protocols is crucial in determining the most efficient paths between source and destination nodes, ensuring the timely delivery of messages. Collaborative agreements with MANETs improve accessibility, allow for partial packet delivery and manage network load, ultimately minimizing delays and contributing to exceptional carrier performance. This article conducts a comparative analysis of simulation parameters for AODV, DSR, and MP-OLSR protocols to explore QoS limitations associated with different routing protocols. The study primarily focuses on evaluating various quality metrics for service improvement, assessing protocol performance. Simulation results underscore the DSR protocol's 80% superior throughput compared to AODV and MP-OLSR. However, in terms of delay and packet delivery ratio, the hybrid protocol outperforms both AODV and DSR protocols. These findings provide a distinct perspective for testing the compliance services of MANETs.
移动 Ad Hoc 网络（MANET）是一种动态组织的无线网络，由于带宽的限制和不断变化的网络拓扑结构，对建立服务质量（QoS）提出了挑战。这些网络是通过系统地组装节点而创建的，缺乏中央基础设施，并动态地连接移动电话和平板电脑等设备。节点采用多种方法提供服务，同时优先考虑网络性能。协议的有效性对于确定源节点和目的节点之间最有效的路径、确保信息的及时传递至关重要。与城域网的协作协议可提高可访问性，允许部分数据包传送，并管理网络负载，最终最大限度地减少延迟，为实现卓越的载波性能做出贡献。本文对 AODV、DSR 和 MP-OLSR 协议的仿真参数进行了比较分析，以探讨与不同路由协议相关的 QoS 限制。研究的主要重点是评估用于改善服务的各种质量指标，评估协议性能。仿真结果表明，与 AODV 和 MP-OLSR 相比，DSR 协议的吞吐量高出 80%。不过，在延迟和数据包传送率方面，混合协议的表现优于 AODV 和 DSR 协议。这些发现为测试城域网的合规性服务提供了一个独特的视角。
{"title":"Improving Mobile Ad hoc Networks through an investigation of AODV, DSR, and MP-OLSR Routing Protocols","authors":"Hameed Khan, Kamal K. Kushwah, Jitendra S Thakur, G. Soni, Abhishek Tripathi","doi":"10.4108/eetsis.5686","DOIUrl":"https://doi.org/10.4108/eetsis.5686","url":null,"abstract":" \u0000Mobile Ad Hoc Networks (MANETs) pose a dynamically organized wireless network, posing a challenge to establishing quality of service (QoS) due to limitations in bandwidth and the ever-changing network topology. These networks are created by assembling nodes systematically, lacking a central infrastructure, and dynamically linking devices such as mobile phones and tablets. Nodes employ diverse methods for service delivery, all while giving priority to network performance. The effectiveness of protocols is crucial in determining the most efficient paths between source and destination nodes, ensuring the timely delivery of messages. Collaborative agreements with MANETs improve accessibility, allow for partial packet delivery and manage network load, ultimately minimizing delays and contributing to exceptional carrier performance. This article conducts a comparative analysis of simulation parameters for AODV, DSR, and MP-OLSR protocols to explore QoS limitations associated with different routing protocols. The study primarily focuses on evaluating various quality metrics for service improvement, assessing protocol performance. Simulation results underscore the DSR protocol's 80% superior throughput compared to AODV and MP-OLSR. However, in terms of delay and packet delivery ratio, the hybrid protocol outperforms both AODV and DSR protocols. These findings provide a distinct perspective for testing the compliance services of MANETs.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"122 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140731578","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 paper addresses the integration of intelligent technology in university physical education, highlighting the need for improved analysis methods for sports equipment image recognition apps to enhance teaching quality.OBJECTIVES: The study aims to develop a more accurate and efficient APP use analysis method for sports equipment image recognition, utilizing intelligent optimization algorithms and kernel limit learning machines.METHODS: The proposed method involves constructing an APP usage effect analysis index system, improving kernel limit learning machines through talent mining algorithms, and validating the model using user behavior data. The method integrates a talent mining algorithm to enhance the kernel limit learning machine (KELM). This integration aims to refine the learning machine’s ability to accurately analyze the large datasets generated by the APP's use, optimizing the parameters to improve prediction accuracy and processing speed.RESULTS: Preliminary tests on the sports equipment image intelligent recognition response APP demonstrate improved accuracy and efficiency in analyzing the APP's usage effects in physical education settings. The study compares the performance of the TDA-KELM algorithm with other algorithms like ELM, KELM, GWO-KELM, SOA-KELM, and AOA-KELM. The TDA-KELM algorithm showed the smallest relative error of 0.025 and a minimal time of 0.0025, indicating higher accuracy and efficiency. The analysis highlighted that the TDA-KELM algorithm outperformed others in analyzing the usage effects of sports equipment image recognition apps, with lower errors and faster processing times.CONCLUSION: The study successfully develops an enhanced APP use analysis method, showcasing potential for more accurate and real-time analysis in the application of sports equipment image recognition in physical education.
{"title":"Application of Sports Equipment Image Intelligent Recognition Response APP in Sports Training and Teaching","authors":"Yang Ju","doi":"10.4108/eetsis.5470","DOIUrl":"https://doi.org/10.4108/eetsis.5470","url":null,"abstract":"INTRODUCTION: The paper addresses the integration of intelligent technology in university physical education, highlighting the need for improved analysis methods for sports equipment image recognition apps to enhance teaching quality.OBJECTIVES: The study aims to develop a more accurate and efficient APP use analysis method for sports equipment image recognition, utilizing intelligent optimization algorithms and kernel limit learning machines.METHODS: The proposed method involves constructing an APP usage effect analysis index system, improving kernel limit learning machines through talent mining algorithms, and validating the model using user behavior data. The method integrates a talent mining algorithm to enhance the kernel limit learning machine (KELM). This integration aims to refine the learning machine’s ability to accurately analyze the large datasets generated by the APP's use, optimizing the parameters to improve prediction accuracy and processing speed.RESULTS: Preliminary tests on the sports equipment image intelligent recognition response APP demonstrate improved accuracy and efficiency in analyzing the APP's usage effects in physical education settings. The study compares the performance of the TDA-KELM algorithm with other algorithms like ELM, KELM, GWO-KELM, SOA-KELM, and AOA-KELM. The TDA-KELM algorithm showed the smallest relative error of 0.025 and a minimal time of 0.0025, indicating higher accuracy and efficiency. The analysis highlighted that the TDA-KELM algorithm outperformed others in analyzing the usage effects of sports equipment image recognition apps, with lower errors and faster processing times.CONCLUSION: The study successfully develops an enhanced APP use analysis method, showcasing potential for more accurate and real-time analysis in the application of sports equipment image recognition in physical education. ","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"28 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140741196","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 today's information age, user interface design and interaction experience are crucial to the success of online platforms.OBJECTIVES: Through in-depth analysis of the user interface design features and user interaction experience of the "Duolingo" platform, this study reveals the potential correlation between them and proposes effective improvement methods to enhance user satisfaction and efficiency.METHODS: Interaction design principles were adopted to guide the improvement and optimization of the user interface. These principles include usability, consistency, and feedback to improve overall user satisfaction with the platform by actively considering user behavior and needs in the design. At the same time, specific mathematical models and equations are used to quantitatively analyze the efficiency and smoothness of the user interaction process, providing designers with more precise directions for improvement.RESULTS: Optimized user interface design and interaction experience can significantly improve user satisfaction and usage efficiency. Users operate the platform more smoothly, which provides useful reference and guidance for the design and development of e-learning platforms.CONCLUSION: Through in-depth analysis of the case of the "Duolingo" platform and the introduction of user experience evaluation methods and interaction design principles, this study has come up with a series of effective improvement measures and verified their effectiveness through experiments. It has certain theoretical and practical significance for improving the user experience of online learning platforms and promoting the design and development of Internet products.
{"title":"Research on User Interface Design and Interaction Experience: A Case Study from \"Duolingo\" Platform","authors":"Yan Qi, Rui Xu","doi":"10.4108/eetsis.5461","DOIUrl":"https://doi.org/10.4108/eetsis.5461","url":null,"abstract":"INTRODUCTION: In today's information age, user interface design and interaction experience are crucial to the success of online platforms.OBJECTIVES: Through in-depth analysis of the user interface design features and user interaction experience of the \"Duolingo\" platform, this study reveals the potential correlation between them and proposes effective improvement methods to enhance user satisfaction and efficiency.METHODS: Interaction design principles were adopted to guide the improvement and optimization of the user interface. These principles include usability, consistency, and feedback to improve overall user satisfaction with the platform by actively considering user behavior and needs in the design. At the same time, specific mathematical models and equations are used to quantitatively analyze the efficiency and smoothness of the user interaction process, providing designers with more precise directions for improvement.RESULTS: Optimized user interface design and interaction experience can significantly improve user satisfaction and usage efficiency. Users operate the platform more smoothly, which provides useful reference and guidance for the design and development of e-learning platforms.CONCLUSION: Through in-depth analysis of the case of the \"Duolingo\" platform and the introduction of user experience evaluation methods and interaction design principles, this study has come up with a series of effective improvement measures and verified their effectiveness through experiments. It has certain theoretical and practical significance for improving the user experience of online learning platforms and promoting the design and development of Internet products.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140746298","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: With the rise of social media platforms, the prevalence of truculent posts has become a major concern. These posts, which exhibit anger, aggression, or rudeness, not only foster a hostile environment but also have the potential to stir up harm and violence. OBJECTIVES: It is essential to create efficient algorithms for detecting virulent posts so that they can recognise and delete such content from social media sites automatically. In order to improve accuracy and efficiency, this study evaluates the state-of-the-art in truculent post detection techniques and suggests a unique method that combines deep learning and natural language processing. The major goal of the proposed methodology is to successfully regulate hostile social media posts by keeping an eye on them. METHODS: In order to effectively identify the class labels and create a deep-learning method, we concentrated on comprehending the negation words, sarcasm, and irony using the LSTM model. We used multilingual BERT to produce precise word embedding and deliver semantic data. The phrases were also thoroughly tokenized, taking into consideration the Hindi language, thanks to the assistance of the Indic NLP library. RESULTS: The F1 scores for the various classes are given in the "Proposed approach” as follows: 84.22 for non-hostile, 49.26 for hostile, 68.69 for hatred, 49.81 for fake, and 39.92 for offensive CONCLUSION: We focused on understanding the negation words, sarcasm and irony using the LSTM model, to classify the class labels accurately and build a deep-learning strategy.
{"title":"Truculent Post Analysis for Hindi Text","authors":"Mitali Agarwal, Poorvi Sahu, Nisha Singh, Jasleen, Puneet Sinha, Rahul Kumar Singh","doi":"10.4108/eetsis.5641","DOIUrl":"https://doi.org/10.4108/eetsis.5641","url":null,"abstract":"INTRODUCTION: With the rise of social media platforms, the prevalence of truculent posts has become a major concern. These posts, which exhibit anger, aggression, or rudeness, not only foster a hostile environment but also have the potential to stir up harm and violence. \u0000OBJECTIVES: It is essential to create efficient algorithms for detecting virulent posts so that they can recognise and delete such content from social media sites automatically. In order to improve accuracy and efficiency, this study evaluates the state-of-the-art in truculent post detection techniques and suggests a unique method that combines deep learning and natural language processing. The major goal of the proposed methodology is to successfully regulate hostile social media posts by keeping an eye on them. \u0000METHODS: In order to effectively identify the class labels and create a deep-learning method, we concentrated on comprehending the negation words, sarcasm, and irony using the LSTM model. We used multilingual BERT to produce precise word embedding and deliver semantic data. The phrases were also thoroughly tokenized, taking into consideration the Hindi language, thanks to the assistance of the Indic NLP library. \u0000RESULTS: The F1 scores for the various classes are given in the \"Proposed approach” as follows: 84.22 for non-hostile, 49.26 for hostile, 68.69 for hatred, 49.81 for fake, and 39.92 for offensive \u0000CONCLUSION: We focused on understanding the negation words, sarcasm and irony using the LSTM model, to classify the class labels accurately and build a deep-learning strategy.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"14 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140745681","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: With the rapid development of science and technology, intelligent painting exhibitions have gradually attracted people's attention with their unique forms. This study aims to create an interactive art space using Internet of Things (IoT) technology to provide audiences with a more prosperous and deeper art experience. OBJECTIVES: The primary purpose of this study is to explore how to use IoT technology to transform a painting exhibition into a digital space that can interact with the audience. By fusing art and technology, the researchers aim to promote innovation in traditional art presentations and stimulate the audience's freshness and interest in art.METHODS: In the Smart Painting exhibition, the researchers used advanced Internet of Things (IoT) technology to incorporate the audience's movements, emotions, and feedback into the artworks through sensors, wearable devices, and cloud computing. The digital devices in the exhibition space could sense the audience's presence and generate and adjust the art content in real-time according to their movements or emotional state, creating a unique display that interacted with the audience. RESULTS: After implementing the Smart Painting exhibition, the audience's sense of participation and immersion in the art display was significantly increased. Through IoT technology, viewers can interact with the artwork in real-time and feel a more personalized art experience. The digitized exhibition space provided the audience a new level of perception, deepening their understanding and appreciation of the artworks. CONCLUSION: This study demonstrates the feasibility of using IoT technology to create interactive art spaces and shows that this innovation can inject new vitality into traditional painting exhibitions. Through digitalization, the interactivity of the art space is enhanced, providing the audience with a more profound art experience. This approach provides artists with new possibilities for creativity and opens up a fresh vision of participatory art for the audience. The Smart Painting Exhibition is expected to become a new model for integrating art and technology, pushing the art world towards a more innovative and open future.
{"title":"Smart Painting Exhibitions: Utilizing Internet of Things Technology Creating Interactive Art Spaces","authors":"Xiaoyan Peng, Chuang Chen","doi":"10.4108/eetsis.5375","DOIUrl":"https://doi.org/10.4108/eetsis.5375","url":null,"abstract":"INTRODUCTION: With the rapid development of science and technology, intelligent painting exhibitions have gradually attracted people's attention with their unique forms. This study aims to create an interactive art space using Internet of Things (IoT) technology to provide audiences with a more prosperous and deeper art experience. OBJECTIVES: The primary purpose of this study is to explore how to use IoT technology to transform a painting exhibition into a digital space that can interact with the audience. By fusing art and technology, the researchers aim to promote innovation in traditional art presentations and stimulate the audience's freshness and interest in art.METHODS: In the Smart Painting exhibition, the researchers used advanced Internet of Things (IoT) technology to incorporate the audience's movements, emotions, and feedback into the artworks through sensors, wearable devices, and cloud computing. The digital devices in the exhibition space could sense the audience's presence and generate and adjust the art content in real-time according to their movements or emotional state, creating a unique display that interacted with the audience. RESULTS: After implementing the Smart Painting exhibition, the audience's sense of participation and immersion in the art display was significantly increased. Through IoT technology, viewers can interact with the artwork in real-time and feel a more personalized art experience. The digitized exhibition space provided the audience a new level of perception, deepening their understanding and appreciation of the artworks. CONCLUSION: This study demonstrates the feasibility of using IoT technology to create interactive art spaces and shows that this innovation can inject new vitality into traditional painting exhibitions. Through digitalization, the interactivity of the art space is enhanced, providing the audience with a more profound art experience. This approach provides artists with new possibilities for creativity and opens up a fresh vision of participatory art for the audience. The Smart Painting Exhibition is expected to become a new model for integrating art and technology, pushing the art world towards a more innovative and open future. ","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"10 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140745818","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}
Kashish Bhurani, Aashna Dogra, Prerna Agarwal, P. Shrivastava, Thipendra P Singh, Mohit Bhandwal
INTRODUCTION: Data integrity protection has become a significant priority for both consumers and organizations as cloud storage alternatives have multiplied since they provide scalable solutions for individuals and organizations alike. Traditional cloud storage systems need to find new ways to increase security because they are prone to data modification and unauthorized access thus causing data breaches. OBJECTIVES: The main objective of this study is to review usage of smart contracts and blockchain technology to ensure data integrity in cloud storage. METHODS: . Case studies, performance evaluations, and a thorough literature review are all used to demonstrate the effectiveness of the suggested system. RESULTS: This research has unveiled a revolutionary approach that capitalizes on the fusion of smart contracts and cloud storage, fortified by blockchain technology. CONCLUSION: This theoretical analysis demonstrate that smart contracts offer a dependable and scalable mechanism for maintaining data integrity in cloud storage, opening up a promising area for further research and practical application.
{"title":"Smart Contracts for Ensuring Data Integrity in Cloud Storage with Blockchain","authors":"Kashish Bhurani, Aashna Dogra, Prerna Agarwal, P. Shrivastava, Thipendra P Singh, Mohit Bhandwal","doi":"10.4108/eetsis.5633","DOIUrl":"https://doi.org/10.4108/eetsis.5633","url":null,"abstract":"INTRODUCTION: Data integrity protection has become a significant priority for both consumers and organizations as cloud storage alternatives have multiplied since they provide scalable solutions for individuals and organizations alike. Traditional cloud storage systems need to find new ways to increase security because they are prone to data modification and unauthorized access thus causing data breaches. \u0000OBJECTIVES: The main objective of this study is to review usage of smart contracts and blockchain technology to ensure data integrity in cloud storage. \u0000METHODS: . Case studies, performance evaluations, and a thorough literature review are all used to demonstrate the effectiveness of the suggested system. \u0000RESULTS: This research has unveiled a revolutionary approach that capitalizes on the fusion of smart contracts and cloud storage, fortified by blockchain technology. \u0000CONCLUSION: This theoretical analysis demonstrate that smart contracts offer a dependable and scalable mechanism for maintaining data integrity in cloud storage, opening up a promising area for further research and practical application.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"21 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140741322","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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