https://publications.eai.eu/index.php/sc <p>EAI Endorsed Transactions on Smart Cities is open access, peer-reviewed scholarly journal focused on applications for Smart Cities with leverage on big-data applications, ICT devices used in the factory of the future, HPC, industrial processes, energy efficiency systems, social platforms, and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a quarterly frequency (four issues per year). Authors are not charged for article submission and processing.</p> <p><strong>INDEXING</strong>: DOAJ, CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions</p> European Alliance for Innovation (EAI) en-US EAI Endorsed Transactions on Smart Cities 2518-3893 <p>This is an open access article distributed under the terms of the <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a>, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.</p> https://publications.eai.eu/index.php/sc/article/view/5860 <p>INTRODUCTION: With the development of the Internet, users tend to express their opinions and emotions through text, visual and/or audio content. This has increased the interest in multimodal analysis methods. <br>OBJECTIVES: This study addresses multimodal sentiment analysis on tweets related to natural disasters by combining textual and visual embeddings.<br>METHODS: The use of textual representations together with the emotional expressions of the visual content provides a more comprehensive analysis. To investigate the impact of high-level visual and texual features, a three-layer neural network is used in the study, where the first two layers collect features from different modalities and the third layer is used to analyze sentiments. <br>RESULTS: According to experimental tests on our dataset, the highest performance values (77% Accuracy, 71% F1-score) are achieved by using the CLIP model in the image and the RoBERTa model in the text. <br>CONCLUSION: Such analyzes can be used in different application areas such as agencies, advertising, social/digital media content producers, humanitarian aid organizations and can provide important information in terms of social awareness.</p> Sefa Dursun Süleyman Eken Copyright (c) 2024 Sefa Dursun, Süleyman Eken https://creativecommons.org/licenses/by-nc-sa/4.0 2024-11-13 2024-11-13 7 4 10.4108/eetsc.5860 https://publications.eai.eu/index.php/sc/article/view/7286 <div class="page" title="Page 1"><div class="section"><div class="layoutArea"><div class="column"><p>Effective crop production and harvesting decisions rely on proper farm monitoring and management. Each region has distinct needs for farm oversight, but the primary focus remains on collecting and evaluating environmental data such as temperature, soil moisture, air humidity, all of which are vital to plant growth. Gathering this data on a large scale requires significant effort and is often based on intuition or simple measurement tools. This paper proposes a novel solution for farming data collection using an IoT platform integrated Long-Range Wide Area Networks (LoRaWAN) network application with Augmented Reality (AR) technology and Machine Learning (ML) algorithms to predict key environmental daily indexes. In a pilot study in Quang Tho, Vietnam, the system accurately predicted environmental conditions, reduced the risk of crop failure, and improved farm management efficiency. This approach enhances real-time data interaction and offers predictive analytics, supporting sustainable agriculture.</p></div></div></div></div> Do Thanh Huong Nguyen Thi Hang Duy Pham Vu Minh Tu Huu Hoang Hanh Kou Yamada Copyright (c) 2024 Do Thanh Huong, Nguyen Thi Hang Duy, Pham Vu Minh Tu, Huu Hoang Hanh, Kou Yamada https://creativecommons.org/licenses/by-nc-sa/4.0 2024-11-20 2024-11-20 7 4 10.4108/eetsc.7286