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Twitter Disaster Prediction Using Different Deep Learning Models

SN computer Science 2024年第1期5卷 1-10页

作者： Duraisamy, Premkumar Natarajan, Yuvaraj Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India

In emergencies, Twitter has become an essential way for people to communicate. Since many people use smartphones, they can easily report events they see. Because of this, more organizations are using Twitter to monitor what is happening digitally. Twitter and similar online platforms provide a place to share their personal experiences and local events with others. People often post about their daily lives, things happening in their neighborhoods, and their activities to keep others informed. In this study, we focus on predicting disasters on Twitter using an improved Bidirectional Encoder Depiction from the Transformers (BERT) model. Twitter has become a crucial communication tool during emergencies, allowing people to report events they witness quickly. As a result, organizations are increasingly using Twitter for digital monitoring. Our research utilizes a modified version of the BERT model, a powerful language representation technique. This modified BERT model enables us to understand better tweets related to disasters and improve the accuracy of our predictions. Using Twitter data, we train our improved BERT model to recognize patterns and signals associated with different types of disasters, such as natural calamities or accidents. We aim to efficiently detect and respond to potential disasters in real time by doing so. Our experiments' results demonstrate our BERT effectiveness, showing improved disaster prediction performance compared to traditional models like the long short-term memory model (LSTM) and gated recurrent unit model (GRU). This research contributes valuable insights into disaster management strategies and allows leveraging social media data in disaster preparedness and response efforts. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Bidirectional encoder depiction from the transformers Gated recurrent unit model Long short-term memory model

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WasteLess: A Cross Platform Mobile Application to Tackle Food Wastage

WasteLess: A Cross Platform Mobile Application to Tackle Foo...

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2024 Arab ICT Conference, AICTC 2024

作者： Karani, Hamza Bhutkar, Ganesh Sayyed, Juwairia Khatib, Fatima Ansari, Abu Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350342086

Waste management has become critical in the twenty-first century and demands immediate attention, particularly with regard to food waste management. With rising waste in landfills and billions of dollars in government losses, a solution is desperately required. This research paper presents an Information and Communication technology (ICT)-based approach to reduce food wastage and promote responsible purchasing of consumables. The proposed solution is a cross-platform mobile application that allows for the donation of excess consumables to those in need, minimizing the amount of food waste produced. The system can be further improved by including wastage statistics and incentivizing users with a point system. © 2024 IEEE.

关键词： Consumables Cross Platform Application Food Wastage Mobile Application Waste Management

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PerceptiGuard: Unveiling DeepFakes Through Enhanced Media Perception

PerceptiGuard: Unveiling DeepFakes Through Enhanced Media Pe...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Phatangare, Sheetal Tekade, Om Thakare, Prathamesh Wankhade, Abhishek Wankhade, Purva Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331540821

This research presents a comprehensive approach to developing a multi-modal deepfake detection system leveraging advanced deep learning architectures. The primary objectives include the creation of a robust detection model, evaluation across various manipulation techniques, and integration into practical applications. Utilizing state-of-the-art neural networks, the system aims to enhance detection accuracy and offer a user-friendly interface for real-world deployment. The proposed architecture incorporates preprocessing steps such as video splitting, detection, and face cropping, followed by feature extraction and video classification using a combination of ResNet and LSTM networks. The model's performance is assessed through confusion matrix evaluation, ensuring reliability across diverse deepfake scenarios. © 2024 IEEE.

关键词： Generative adversarial networks

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CulturalEcho and Educator App for Tourists 4

CulturalEcho and Educator App for Tourists

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Kulkarni, Radhika V. Suryawanshi, Snehal Shirsath, Aditya Sonone, Swati Phapale, Siddhi Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331518981

Language barriers frequently stand in the way of successful communication in a society where languages are widely spoken. Moreover, the limited availability of educational resources on Indian history and culture hinders learning and cross-cultural exchange. The CulturalEcho AR Translator employs augmented reality (AR) technology to facilitate real-time translation of text written in Indian languages when viewed through a device's camera. The system utilizes Optical Character Recognition (OCR) technology to identify the text in Indian languages and translates it into English. Users can quickly comprehend and interact with the content in English. Additionally, the application features a comprehensive content management system that includes a diverse collection of multimedia, historical, and cultural articles and images about India. The AR interface enables users to explore this educational archive, fostering interactive learning experiences enriched with cultural insights. The inclusion of a chatbot further enhances the experience by providing assistance and information, addressing user questions related to translations and cultural topics. The system employs authentication methods, including verified language databases sourced from reputable language authorities and government entities, to ensure the accuracy and legitimacy of translations. Additionally, it offers virtual tours of popular tourist attractions and historical landmarks to enhance the customer experience. With interactive 3D models and panoramas, users can explore historical sites and learn about their significance, deepening their understanding of Indian heritage and culture. © 2024 IEEE.

关键词： Optical character recognition

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AI-Generated Video Forgery Detection and Authentication 9

AI-Generated Video Forgery Detection and Authentication

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Tiwari, Ayush Kumar Sharma, Aman Rayakar, Poonam Bhavriya, Manish Kumar Nisha Army Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350394474

Deep learning has a variety of uses and issues it can solve in the real world, but it also has some limitations. The growing use of AI-Morped Videos is one of the most recent and complicated issues. "AI Morphed Videos,"which are digitally manipulated still or moving visuals, are made using deep learning techniques. In an AI Morphed Video, the target's face is superimposed over the original image so that the altered digital data can be used for online frauds, extortion, pornography, etc. It is getting harder and harder to manually discern between true and false as deep learning develops. Therefore, research and development in the field of AI Morphed Video detection are crucial. An overview of the several AI Morphed Video detection strategies is completed in time for the classification of feature-based, temporal-based, and deep feature-based AI Morphed Video detection. The comparison research is based mostly on the key features used, including the face detection architecture, the deep learning architecture, whether it is video-based or image-based, the dataset used, the frames size, and the dataset size used. Along with the comparison, a semi-supervised GAN architecture is also proposed and built to recognize the AI Morphed Video. © 2024 IEEE.

关键词： Generative adversarial networks

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CAAM- CNN With Autoencoder Attention Mechanism for Recommendation System to Improve Trust in E-commerce Industry 2

CAAM- CNN With Autoencoder Attention Mechanism for Recommend...

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2nd IEEE International Conference for Women in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Sethi, Vikas Kumar, Rajneesh Mehla, Stuti Department of Computer Science and Engineering Ambala Mullana India Panipat Institute of Engineering and Technology Department of Computer Science and Engineering Panipat India

ISBN: (纸本)9798350383287

COVID-19 crisis expedited the spread of e-commerce, moving beyond luxury goods and services and towards new businesses, product categories and basic necessities that are important to many people. Customers can have access to a wide range of products from the comfort and security of their homes but still have trusting issues while purchasing online. Online feedback either comments or ratings given by customers, has the power to affect how consumers make purchasing decisions. This meteoric expansion of digitized information and number of users over internet brought the use of recommender systems as a significant solution by offering more proactive and individualized information services and enable users to quickly narrow their choices and make wise ***, the majority of existing recommendation models have poor accuracy issues as a result of data scarcity and cold start issues because recommender systems only have a limited amount of explicit data and this also lead to trust issue where users are fear of being taken advantage due to misleading information on the e-commerce websites. To solve this problem and provide quality recommendations, a new trust-based approach CNN with Autoencoder Attention Mechanism (CAAM) is proposed. CAAM uses static and dynamic trust combined with the user-item preferences and pass through the auto-encoder to reduce noise and make encoded features visible. After that convolution is used to combine encoded features with user and item features in latent space to provide nonlinear mapping and increase the reliability of features and to improve the recommendations which ultimately lead to improve the trust of people over *** Epinions dataset is used to evaluate the concept in a number of measures. © 2024 IEEE.

关键词： Recommender systems

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A Novel Approach for Optimal Cluster Identification and N-Order Hesitation Based Time Series Forecasting

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SN computer Science 2024年第7期5卷 1-14页

作者： Dixit, Ankit Jain, Shikha Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Time series forecasting is an important field of research, especially when the series is completely random, known as a strictly non-stationary time series (NS-TS). To handle the randomness efficiently, the paper presents a novel approach to identify the optimal number of clusters in NS-TS followed by a hesitation-based N-order IFS forecasting algorithm (called IFSH_new). The performance of the proposed approach for determining the optimal number of clusters is demonstrated using four variants of the proposed algorithm, IFSH_new, across five datasets. Furthermore, a comparison of the best variant of the proposed approach with existing state-of-art work based on mean absolute percentage error (MAPE) and root mean square error (RMSE) confirms the superiority of the proposed model over others. Additionally, the graphical representation of actual versus forecasted data points for different datasets shows good improvement at high change points compared to other approaches. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Degree of hesitation Intuitionistic fuzzy set (IFS) N-order forecasting model Non-stationary time series (NS-TS) Time series forecasting model

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Transcription, Translation and Summarization to Improve Educational Understanding 4

Transcription, Translation and Summarization to Improve Educ...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Kulkarni, Radhika V. Shilimkar, Avanish Bandi, Shreyash Bhegade, Sakshi Dadmal, Jidnyasa Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331518981

In-depth research on improving educational comprehension with the use of summarization, translation, and transcription technology is presented in this work. With an emphasis on accuracy and efficiency, a comparative analysis of the different models employed in these processes is carried out. In particular, we investigate the use of Whisper AI for transcription tasks and a Python interface for text summarization when implementing BART (Bidirectional and Auto-Regressive Transformers). Our results show that Whisper AI greatly increases transcription speed and accuracy, whereas BART excels at producing clear and succinct summaries. Better educational content distribution can be enabled by incorporating these modern techniques, which will make the material easier for teachers and students to understand. This research has many kinds of practical applications, from translating instructional materials between languages to producing succinct summarized notes and then providing flashcards and quizzes based on that. © 2024 IEEE.

关键词： Distribution transformers

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Grid-Interactive Novel Resilient Control of Solar PVWind-Battery Storage Microgrid Under Distorted and Unbalanced Grid Voltages

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1744-1757页

作者： Gali, Vijayakumar Jamwal, Prashant Kumar Gupta, Nitin Babu, B. Chitti Ahmadi, Mohammad Jawid Morey, Meghraj Sudhakar University of Hertfordshire School of Physics Engineering and Computer Science Department of Electrical and Electronics Engineering HatfieldAL109AB United Kingdom Nazarbayev University Department of Electrical and Computer Engineering Astana Kazakhstan Malaviya National Institute of Technology Department of Electrical Engineering Jaipur India Indian Institute of Information Technology Design and Manufacturing Department of Electronics and Communication Kancheepuram India

Integrating solar photovoltaic (PV), wind, and battery storage (BS) systems into the grid introduces significant power quality (PQ) challenges. In particular, the intermittent nature of solar PV and wind energy systems (WES), combined with nonlinear loads, can lead to grid instability. As a result, maintaining a reliable and high-quality power supply to consumers becomes a substantial challenge. This article presents two novel solutions to address these challenges: the anti-windup mixed-order generalized integrator (AWMOGI) and the adaptive delay operation period filter (ADOPF). The AWMOGI effectively extracts fundamental components from distorted utility grid voltages, minimizing DC offset and reducing steady-state oscillations. Meanwhile, the ADOPF swiftly captures positive sequence voltage components by introducing a two-sampling-interval delay in the d-q coordinate system. The hierarchical-loop controller parameters are optimized using a customized Harris Hawks Optimization (HHO) algorithm, which enhances error reduction and facilitates rapid establishment of reference values for DC-link voltage loop control. An adaptive dynamic power management scheme also enables the microgrid (MG) to manage surplus power from MG sources, reducing stress on the BS and ensuring DC-link voltage stability. The experimental results demonstrate significant improvements in handling utility grid abnormalities, dynamic loads, and intermittent microgrid source operation. The experimental results show a zero DC-off set and steady-state errors, a fast computational time of 35e-5s, and the THD of grid currents of 3.16%, which complies with the IEEE 519 standards. © 1975-2011 IEEE.

关键词： Adaptive filters

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Enhancing Public Safety through Real-time Weapon Detection: A Deep Learning Approach 3

Enhancing Public Safety through Real-time Weapon Detection: ...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Dass, J. Maria Arockia Suresh, A. Swathi, K. Shalini, Paidimuddala Sinha, N A Yaswanth Kotholla Uday Kiran, U. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh Chittoor India

ISBN: (纸本)9798350354171

Date In response to the imperative need for mitigating criminal activities and ensuring public safety, this research proposes a novel approach leveraging deep learning techniques for real-time weapon detection. In contemporary society, criminal acts pose significant threats to both individuals and the broader community, necessitating proactive measures to counter such offenses swiftly. By harnessing advanced technologies, particularly deep learning algorithms like YOLOv7-tiny, this study intend to create a robust system capable of identifying weapons in surveillance footage captured by CCTV cameras. The project begins with the meticulous collection of datasets from reputable research sources, ensuring diverse and comprehensive samples for training the deep learning model. Subsequently, these datasets undergo rigorous pre-processing steps to optimize them for training, including resizing images to the required format. To facilitate accurate annotation and labelling of the dataset, advanced tools like Robo flow software are employed, streamlining the process and enhancing efficiency. Upon completion of the pre-processing phase, the YOLOv7-tiny algorithm is deployed for training the model on the annotated datasets. Through iterative training and validation processes, the model is fine-tuned to achieve optimal performance in weapon detection tasks. Once trained, the model is capable of swiftly and accurately identifying weapons in real-time surveillance footage. This project presents an effective solution for real-time weapon detection in public spaces, leveraging deep learning algorithms and advanced software tools. © 2024 IEEE.

关键词： Image annotation

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