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Topic Modeling Based Clustering of Disaster Tweets Using BERTopic

Topic Modeling Based Clustering of Disaster Tweets Using BER...

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2024 MIT Art, Design and technology School of Computing International Conference, MITADTSoCiCon 2024

作者： Rachel J, Julanta Leela Bhuvaneswari, A. Kumudha, M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India T.John Institute of Technology Dept. of Information Science and Engineering Bangalore India

ISBN: (纸本)9798350362879

The essence of catastrophe management is in the capacity to promptly and effectively addressing unforeseen circumstances, as well as to implement premeditated and flexible strategies in accordance with established protocols and norms. Twitter has become a prominent means of disaster communication, particularly in nations where it has a substantial presence. Twitter can provide valuable information regarding the effects of infrastructure on emergency management in the event of significant disasters. However, the process of sifting through numerous unrelated tweets can be laborious and time-consuming. Prior research has successfully recognized the various categories of communications present on social media platforms during times of disaster. However, there is a dearth of recommended methodologies aimed at effectively extracting valuable information from these messages. In this study, we propose a framework that may be efficiently utilized to promptly deliver disaster effect information derived from social media sources. The methodology involves the application of certain techniques to process the unprocessed Twitter data by employing keyword filtering, location-based filtering, and analysis of tweet properties. Subsequently, Bidirectional Encoder Representations from Transformers (BERT) technique is utilized to categorize the tweets originating from disaster-Affected regions into distinct subjects that are relevant and valuable for emergency management. BERT analysis yielded a total of 376 themes within the dataset. Among these topics, a significant proportion of 196 were found to be closely associated with the impacts of disasters. These impacts encompassed many aspects such as outages, closures, flooded roadways, and damaged infrastructure. © 2024 IEEE.

关键词： Disasters

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An Enhanced Smart Intelligent Detecting and Alerting System for Industrial Gas Leakage using IoT in Sensor Network 5

An Enhanced Smart Intelligent Detecting and Alerting System ...

引用

5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Senthil, G.A. Suganthi, P. Prabha, R. Madhumathi, M. Prabhu, S. Sridevi, S. Agni College of Technology Dept. of Information Technology Chennai India Sri Sai Ram Institute of Technology Dept. of Electronics and Communication Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Dept. of Artificial Intelligence and Data Science Chennai India Sri Ramachandra Engineering and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665474672

Leakage of any type of gas has emerged as a hazard in recent years, whether in a typical workplace, boiler, a commercial setting, a café, or a canteen, industries natural gas, coal, refinery products, crude oil, cement, electricity, steel, and fertilizers etc. In this article, I proposed a new invention of an IoT-based gas waste monitoring, leak detection, and alerting system. The research extends on the development of such an intelligent system that can intelligently preserve gas, ensure the safety precaution and pre vent accidents. The technique must be connected to the stove. The design incorporates ultrasonic sensors which identify bio gas leakage in circumstance with in square metre is usage area of industries or cooking place. The Proposed work smart intelligence system is cuts off the gas supply using an automated shutting off device. Whenever a gas leak is detected, consumers will be notified through SMS via GSM, location using GPRS, and will be able to resolve the situation as quickly as feasible. The flame sensor in the technology will supervise flame and fire. The buzzer starts to sound when a fire is detected. Aside from that, the system may save data on the cloud. With the help of this cloud storage solution, every user's daily gas use may be traced. Finally, this approach will aid in recognizing per user natural gas consumption. The system has been examined and is capable of monitoring gas loss, evacuation, and sending an SMS notification and location aid to the user. The precipitate demonstrated its usefulness in conserving a considerable amount of domestically squandered gas. © 2023 IEEE.

关键词： Liquefied petroleum gas

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Enhanced Hybrid Deep Learning Model for Distributed Denial of Service Attack Detection in IoT Environments

Enhanced Hybrid Deep Learning Model for Distributed Denial o...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sharmila Kumari, N. Vimala, H.S. Pruthvi, C.N. Shreyas, J. Uvce Dept. of Cse Bengaluru India Dayananda Sagar College of Engineering Dept. of Cse Bengaluru India Manipal Academy of Higher Education Dept. of Information Technology Manipal Bengaluru India

ISBN: (纸本)9798350375442

Security concerns have grown significantly with the proliferation of Internet of Things (IoT) devices, particularly in light of Distributed Denial of Service (DDoS) attacks. Network resources may be overloaded by these assaults, leading to significant disruptions, data leaks, and outages. Studies have demonstrated that a combined deep learning strategy utilizing Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks may successfully identify these DDoS assaults in Internet of Things environments. This study aims to make the hybrid model better by adding new techniques like feature selection, ensemble learning, and an improved attention mechanism. We thoroughly test the upgraded model against the original hybrid model and other traditional machine learning methods using different datasets. The results show that the improved model boosts detection accuracy, precision, recall, and F1-score, making it a stronger tool for spotting DDoS attacks in IoT networks. By using CIC-DDoS2019 dataset, our model yields results with an accuracy of 99%, precision, recall and F1 score of 98%. © 2024 IEEE.

关键词： Convolutional neural networks

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Investigating the Effectiveness of Artificial Intelligence in Watermarking and Steganography for Digital Media Security 21

Investigating the Effectiveness of Artificial Intelligence i...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Abdalwahid, Shadan M.J.A Hashim, Wassan Adnan Saeed, Mohammed Ganim Altaie, Sarmad A. Kareem, Shahab Wahhab Engineering College Erbil Polytechnic University Information System Engineering Dept. Erbil Iraq AIQAlam University College Medical Instruments Techniques Dept Kirkuk Iraq Dohuk Polytechnic University Department of Information Technology Dohuk Iraq University of Technology Computer Engineering Department Baghdad Iraq Erbil Technical Engineering College Erbil Polytechnic University Information System Engineering Dept. Erbil Iraq

ISBN: (纸本)9798350374131

Watermarking and Steganography are methods of embedding digital information within images or other media, such as text or audio, for the purpose of Copyright Protection or covert communication. This field of study is not recent and has been ongoing for several years, culminating in its current advanced stage. The utilization of Artificial Intelligence algorithms has played a pivotal role in revolutionizing various aspects, including security concerns and the precision of outcomes, as compared to traditional methods. This paper focuses on doing an in-dept. analysis of cutting-edge research, techniques, and methodologies employed in the domain of Watermarking and Steganography, specifically in conjunction with Artificial Intelligence. By thoroughly examining and evaluating a collection of recent studies in this domain, we have scrutinized the outcomes of each study with respect to its research goal, the acquired results, the employed algorithm, and the research's robustness in terms of susceptibility to various forms of attacks and the technique of data embedding. Our findings indicate that the use of Artificial Intelligence algorithms has a substantial influence on enhancing result precision, system resilience, and establishing data ownership. Deep Neural Networks (DNN) are essential in Watermarking and Steganography due to their robustness, effectiveness, and accuracy. Novel methodologies and systems improve security, incorporation rates, precision of detection, and speed of convergence. Deep learning is being investigated in techniques such as data concealment, information hiding, and steganography to improve security. © 2024 IEEE.

关键词： Embeddings

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Deep Learning-Based Optimal Switch Placement in Smart Power Distribution Systems

Deep Learning-Based Optimal Switch Placement in Smart Power ...

引用

2023 IEEE International Conference on Environment and Electrical engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023

作者： Rastegar, Mohammad Ebrahimi, Mehrdad Karami, Kiana Shiraz University Dept. of Power and Control Engineering Shiraz Iran Penn State Harrisburg Dept. of Electrical Engineering and Electrical Engineering Technology Pennsylvania United States

ISBN: (纸本)9798350347432

The reliability of power distribution systems plays a key role in enhancing the service quality of customers. To reach a reliable system, switching devices such as circuit breakers, remote-controlled switches, and manual switches should be optimally installed in power distribution systems. However, due to the nonlinearity of the problem, a large number of candidate installation points, the diversity of decision variables, and the existence of tie lines, traditional mathematical-based switch optimization models may not be practical for large-scale systems. To address this challenge, this paper proposes a novel approach based on deep learning techniques to determine the optimal number and location of switching devices in real-world power distribution systems. We employ an ensemble learning method along with explainable artificial intelligence tools to build an accurate surrogate model. The proposed learning-based method can provide a fast and effective solution to the optimal switch placement problem, without requiring complicated mathematical optimization algorithms. The proposed method is tested on a modified 11 kV power distribution system connected to Bus 4 of the Roy Billiton test system (RBTS-Bus 4). Simulation results demonstrate that the proposed surrogate model outperforms traditional mathematical-based switch optimization models in terms of scalability and computational complexity. © 2023 IEEE.

关键词： Optimization

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Dry Power Transformer Fault Diagnosis Using Deep Learning: A Review

Dry Power Transformer Fault Diagnosis Using Deep Learning: A...

引用

2024 International Conference on Sustainable Power and Energy, ICSPE 2024

作者： Tiwari, Priyanka Singh, Shweta Singh, Vinod Kumar Maharishi University of Information Technology Lucknow Dept of Electrical Engineering UP India SR Group of Institutions Dept. of Electrical Engineering UP Jhansi India

ISBN: (纸本)9798350366051

Dry power transformers to be most effectively used in industrial, commercial, and even utility applications. They are ideal for both low voltage and medium ones hence assuring comfort in providing the needs of utility company. In this article, we summarize the applications of deep learning on vibration-based diagnosis of deformed core detection in dry power transformer based on systematic review from related literature. The perspective of data-driven, that is to say integrating various technologies related with deep learning into traditional on-the-spot fault diagnosis, especially vibration analysis and acoustic signal processing technology;infrared thermal imaging technique coupled with typical diagnostic sinkhole transformation matrix algorithm by using neural network computing. Next, we consider the impact of emerging research in these areas on transformer reliability and discuss several examples from recent literature that show how intelligent systems can be used to improve this. © 2024 IEEE.

关键词： Thermography (imaging)

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Blockchain in Healthcare Systems

Blockchain in Healthcare Systems

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Matthew, Rejo Daga, Reeti Maurya, Mayank Mukesh Patel School of Technology Management and Engineering Nmims University Dept. of Information Technology Mumbai India

ISBN: (纸本)9798350313413

Advancements in technology are reshaping the future of healthcare data management and systems. The imperative for secure, efficient, and scalable Electronic Health Record (EHR) solutions has spurred innovation. Traditional healthcare data management, relying on cloud-based and client-server systems, grapples with issues like single points of failure, data privacy concerns, centralization, and vulnerabilities. Blockchain technology presents a promising solution, offering replication, security, and privacy features that can address these challenges. However, the healthcare sector requires a blockchain that is even more secure, fault-tolerant, traceable, adaptable, and privacy-focused. This paper's core objective is to examine how blockchain technology can be integrated into healthcare systems, with a focus on addressing its inherent challenges. It delves into issues faced by blockchain in healthcare and examines potential solutions. These solutions encompass techniques such as clustering, sharding, improved consensus protocols, and the use of smart contracts to enhance the scalability, security, and privacy of healthcare data management systems. © 2023 IEEE.

关键词： Blockchains Electronic health record (EHR) Scalability Sharding Smart contracts

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Synthesis of Model Predictive control for an Electrohydraulic Servo System Using Orthonormal Functions 5

Synthesis of Model Predictive Control for an Electrohydrauli...

引用

5th IEEE International Conference on Communications, information, Electronic and Energy Systems, CIEES 2024

作者： Mihalev, Georgi Yordanov, Stanimir Ormandzhiev, Krasimir Stoycheva, Hristina Todorov, Todor Technical University of Gabrovo Dept. of Automation Information and Control Systems Gabrovo Bulgaria Technical University of Gabrovo Dept. of Power Engineering Gabrovo Bulgaria Technical University of Gabrovo Dept. of Comunication Technique and Technologies Gabrovo Bulgaria

ISBN: (纸本)9798350352863

The paper presents the implementation of a Continuous-Time Model Predictive control (CMPC) regulator for controlling an electrohydraulic servo system. The control object is a test bench consisting of three modules and a PC-based data acquisition and control system. The system is modeled in state-space form, accounting for parametric uncertainties. The synthesis of the model predictive controller is performed by approximating the control signal using Laguerre orthonormal functions. The article includes mathematical formulas and relationships that illustrate the modeling approach. Simulations and robust analysis of the closed-loop system are conducted. Through a real-time control system, experimental data and graphs are obtained, demonstrating the performance and optimality of the synthesized control strategy. © 2024 IEEE.

关键词： Model predictive control

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Hybrid Framework for Parking Slot Allocation Using Double Auction and Cooperative Game Theory in Fog-Based Systems 3

Hybrid Framework for Parking Slot Allocation Using Double Au...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Bairi, Pijush Swain, Sujata Bandyopadhyay, Anjan Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India Haldia Institute of Technology Dept. of Information Technology Haldia India

ISBN: (纸本)9798331523893

The rapid growth of urban transportation necessitates innovative strategies for efficient parking management. Fog-based smart parking systems help reduce latency and improve resource use, but challenges remain in achieving fair and optimal slot allocation. This study presents a hybrid framework that combines a multi-attribute-based double auction mechanism with cooperative game theory. The double auction allocates parking slots fairly, considering both economic and non-price factors like proximity and reputation, while cooperative game theory fosters user coalitions to boost efficiency and satisfaction. By utilizing fog nodes for real-time processing, the system minimizes cloud reliance, ensuring low latency and scalability. Simulations demonstrate significant gains in efficiency, fairness, and responsiveness, showcasing the framework's potential to enhance resource use and promote collaboration for smarter urban mobility. © 2025 IEEE.

关键词： Urban transportation

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A Self-Supervised Semantic Segmentation Method for Identifying Barrett' s Esophagus in Endoscopic Images 24

A Self-Supervised Semantic Segmentation Method for Identifyi...

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6th Asia Pacific information technology Conference, APIT 2024

作者： Lin, Guo-Shiang Chang, Ku-Yaw Hsu, Wen-Teng Yang, Sheng-Chih Yan, Shen-Lei Dept. of Artificial Intelligence and Computer Engineering National Chin-Yi University of Technology Taiwan Dept. of Computer Science and Information Engineering National Chin-Yi University of Technology Taiwan Dept. of Internal Medicine Chang Bing Show Chwan Memorial Hospital Taiwan

ISBN: (纸本)9798400716218

Barrett's esophagus is considered a precancerous condition that may lead to esophageal cancer. The condition is usually diagnosed through an endoscopy with biopsy. This careful imaging examination is quite labor-intensive and usually lacks of diagnostic consistency. In this paper, a computer-aided diagnosis (CAD) method was proposed to assist pathologist in Barrett's esophagus diagnosis from endoscopic images. The proposed semantic segmentation was built based on the U-Net architecture, which features capturing detailed spatial information and context to produce accurate pixel-wise predictions. An autoencoder, which is quite capable of learning a compact and meaningful representation of the input data, is incorporated to achieve self-supervised learning. A two-stage pretraining for the encoder and the decoder is adopted for better performance and reduced data requirements. The proposed method can extract features and patterns from unlabeled data without requiring human annotation or labels, which is very suitable in many biomedical image analyses. The experimental results show that the segmentation accuracy with mean pixel accuracy, mean dice coefficient, and mIoU reaches 98.20%, 87.96%, and 83.18% respectively, indicating that the proposed method has a good performance on the identification of Barrett's esophagus in endoscopic images. © 2024 Owner/Author.

关键词： Semantic Segmentation

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