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FreqEmo: A Twitter Dataset Comprising Users' Tweeting Frequency and Emotions

FreqEmo: A Twitter Dataset Comprising Users' Tweeting Freque...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Agrawal, Aryan Arora, Anshika Chakraborty, Pinaki Netaji Subhas University of Technology Department of Electronics and Communication Engineering New Delhi India School of Computer Science Engineering and Technology Bennett University Greater Noida India Netaji Subhas University of Technology Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350379716

Social media is now indispensable, used by all social, economic, age, and demographic groups. It provides extensive user-generated data, offering insights into users' mental states. This paper introduces a novel Twitter dataset comprising users' tweeting frequency and emotions shared. The dataset is based on Tweets collected from the USA and India during a 60-day period using adjectives representing emotions. Further, for frequently tweeting users, NRC emotion lexicon has been implemented which extracted sentiment and emotion scores of the users. The final dataset includes users' tweeting patterns along with the distribution of emotions and sentiments in their tweets. This dataset has been made publicly available and this project aims to help researchers develop models to assess users' emotional states based on social media activity without self-reporting, with applications in correlating usage patterns with well-being, clustering users by emotions and tweeting patterns, and analyzing temporal, spatial, and demographic trends. To provide baseline performance, k-means clustering is implemented in this study to cluster the users with similar usage patterns. Clusters are analyzed to determine the types of emotions and sentiments shared by users within each cluster. Based on the analysis, it is concluded that as Twitter usage increases within the clusters, the proportion of users dominated by negative sentiment also rises significantly, peaking at 41.67%. Additionally, the percentage of users whose predominant emotion is joy decreases with increased usage, reaching a low of 8.33% as opposed to the peak value of 69.44% in the group characterized by least usage, while the percentage of users dominated by anger and disgust grows to 17.24% and 4.17% as their usage intensifies. © 2024 IEEE.

关键词： Tweets

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A Deep Analysis on Security Aspects of Cloud Computing Ontologies 5

A Deep Analysis on Security Aspects of Cloud Computing Ontol...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Bisht, Girish Singh Gobi, N. Kapse, Vinod Mansiram Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

The primary objective of this research is to get a comprehensive understanding of Cloud Computing ontologies, their applications, and their specific areas of interest. The proposed research methodology for this systematic review comprises a well-defined research topic, criteria for including and excluding studies, and a comprehensive search strategy to identify relevant source papers for the review. The chosen research is categorized into four main areas: elucidation of cloud resources and services, elaboration of cloud security, clarity of cloud interoperability, and understanding of cloud services discovery and selection. Utilizing ontologies to improve the security and smooth integration of cloud computing services uncovers various challenges and identifies areas for more research. © 2024 IEEE.

关键词： Cloud Computing Cloud service ontology systematic review

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Generative Artificial Intelligence: Principles, Potentials and Challenges 2

Generative Artificial Intelligence: Principles, Potentials a...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Routray, Sudhir K. Jha, Mahesh K. Sharmila, K.P. Javali, Abhishek Pappa, M. Singh, Monika Cmr University Department of Computer Science and Engineering Bangalore India Cmr Institute of Technology Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798350368413

In recent years, we have witnessed the raise of several generative artificial intelligence (AI) products and services which have strong disruptive capabilities. Majority of these AI-based products and services are also very popular since their public availability. These generative AI products and services have a lot of potentials to do the common office works, writing codes, doing accounting tasks, predicting common patterns, generating creative contents, marketing products and services, increasing efficiencies in the systems, translating to different languages, designing products, and many such things. These effects of generative AI are phenomenal and it is going to start a large scale automation in the product design and service sectors. In addition to the benefits, several challenges are associated with generative AI. Starting from the bias to security risks to fake content creation, there are many challenges with these products and services. In this paper, we go through the basic principles of generative AI and then study its present and future prospects. We analyze its perilous effects and the common challenges with a few examples. Overall, generative AI can be beneficial tool if it is used positively under appropriate regulatory systems. © 2024 IEEE.

关键词： Generative adversarial networks

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Perfomance comparison of Machine Learning (ML) based fault detection scheme for a compensated transmission line

Perfomance comparison of Machine Learning (ML) based fault d...

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2024 IEEE PES/IAS PowerAfrica, PowerAfrica 2024

作者： Mazibuko, N. Akindeji, K.T. Moloi, K. University of Technology Smartgrid Research Center Durban Department of Electrical Power Engineering Department of Electronics and Computer Engineering Durban South Africa

ISBN: (纸本)9798350389388

Compensated transmission lines are employed in power system networks to increase energy transmission capacity, improve system stability, decrease transmission losses, and provide more flexibility in power management. However, the effectiveness of protection relays in detecting, classifying, and locating faults in such networks is significantly impacted. This is due to changes in impedance within the fault loop caused by devices used in compensated networks, which alters the apparent impedance seen by the relay. This study introduces three different fault detection and classification schemes for compensated transmission lines. Initially, an IEEE 9-bus system with series compensation at line 4-6 is utilized for data collection. Feature extraction using Principal Component Analysis (PCA) is then conducted, followed by the implementation of fine k-nearest neighbors (KNN), Medium Nuero network (MNN), and SQVM classifiers. A comprehensive performance analysis is carried out, revealing that SQVM achieves the highest overall accuracy of 99.3%, while the fine tree outperforms SQVM in single-line fault detection. © 2024 IEEE.

关键词： Power transmission lines

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Adaptive Intrusion Detection in Cyber-Physical Systems using Reinforcement Learning-Based Autoencoders 2

Adaptive Intrusion Detection in Cyber-Physical Systems using...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Rajathi, N. Divya, S. Anitha Elavarasi, S. Saritha, G. Jeya ramya, V. Department of Information Technology Kumaraguru College of Technology Coimbatore India Department of Computer Science and Business System Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (纸本)9798331504960

The increasing integration of Cyber-Physical Systems (CPS) in critical infrastructure presents unique challenges for ensuring robust cybersecurity. Traditional Intrusion Detection Systems (IDS) often struggle with the dynamic, complex nature of CPS environments, making adaptive solutions essential. This paper presents an Adaptive Intrusion Detection System (AIDS) that leverages Reinforcement Learning-Based Autoencoders (RL-AE) to detect and respond to cyber intrusions in CPS environments. The proposed model combines the power of autoencoders for unsupervised anomaly detection with reinforcement learning to continuously adapt and improve detection accuracy. Experimental results on benchmark CPS datasets demonstrate that the RL-AE-based IDS achieves a detection accuracy of 98.3%, a precision rate of 96.8%, and a recall rate of 97.5%, effectively minimizing false positives to 2.4%. The system also demonstrated rapid adaptability, with a convergence time of less than 50 iterations in response to new attack patterns. These results highlight the model's ability to enhance system resilience and promote secure and efficient CPS operations in industrial and critical infrastructure applications. This approach provides a scalable and flexible IDS solution, promoting secure and resilient CPS operations in real-time, high-stakes environments. © 2024 IEEE.

关键词： Network intrusion

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Improved Chinese Few-Shot relation extraction using Large Language Model for Data Augmentation and Prototypical Network

Improved Chinese Few-Shot relation extraction using Large La...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Haoran, Xu Lou, Kechen Chan, Sixian College of Electronics and Information Engineering Tongji University Shanghai China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

ISBN: (纸本)9781665410205

Chinese few-shot relation extraction aims to effectively identify relationships between entities in text using limited data, which is crucial for information extraction and knowledge construction and assists in extracting critical information from medical cases to support personalized rehabilitation treatment plans. However, due to the limited number of samples, existing methods struggle to capture sufficient relational features from limited data, resulting in poor extraction performance. Therefore, we propose an Improved Chinese Few-Shot Relation Extraction Using Large Language Model for Data Augmentation and Prototypical Network to address this issue. Specifically, we establish the Chinese few-shot relation extraction datasets DUIE Few and SanWen Few. Notably, we introduce a framework based on large language models for dataset augmentation, which effectively alleviates the problem of feature extraction due to insufficient data and improves task performance. Finally, we present baselines for prototype networks, Siamese networks, and the CFSRE model based on relational category information. Experimental results show that the CFSRE model improves accuracy, recall, and F1 score under few-shot conditions, particularly as the sample size decreases. In summary, the method we propose demonstrates promising results in Chinese few-shot relation extraction tasks and holds the potential to advance medical rehabilitation research. © 2024 IEEE.

关键词： Information retrieval

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Enhancement of Photovoltaic Dust Detection Using Deep Learning Technique

Enhancement of Photovoltaic Dust Detection Using Deep Learni...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Gracia, R. Lillian Priyadharsini, C. Caroline, B.Elizabeth Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India Ifet College Of Engineering Division Of Electronics And Communication Engineering Villupuram India

ISBN: (纸本)9798350371567

The dust collection on solar panels situated in solar parks is a common problem faced by PV system operators, particularly in arid and dusty regions. Dust deposition reduces the transparency of the panel surface, leading to decreased sunlight absorption and thus reduced energy conversion efficiency. Traditional methods for detecting dust accumulation involve manual inspection or periodic cleaning schedules, both of which are labor-intensive, time-consuming, and often inefficient. Recent developments in deep learning and machine vision have made autonomous dust detection techniquesfeasible. Numerous techniques have been employed to identify and measure dust on solar panels, such as machine learning models and image processing algorithms. In this paper, the dataset of clean and dusty images of the solar panels utilized wasapplied to four state of the art models and then the EfficientnetB0 model was proposed to detect the dust over the solar panels. On comparing the EfficientnetB0 model with the other state of the art models, EfficientnetB0 yielded an impressive accuracy rate of approximately 85.92%. Further research and development in this field hold the potential to revolutionize the maintenance and operation of solar power plants, contributing to a greener and more sustainable future. © 2024 IEEE.

关键词： Solar panels

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Development of an Application for Detecting Lung Cancer Using Machine Learning

Development of an Application for Detecting Lung Cancer Usin...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Marathe, Abha Makadi, Sahil Meshram, Pratik Mahulkar, Amey Maurya, Ayush Mhaske, Krishna Vishwakarma Institute of Technology Department of Electronics and Telecommunication Engineering Pune India Vishwakarma Institute of Technology Department of Computer Science Pune India

ISBN: (纸本)9798331542344

Lung cancer continues to be a major contributor to cancer-related deaths globally, making it necessary to bring improvements in reliable and accurate detection methods. This study discusses lung cancer detection which uses an open source dataset about patient and 4 ML algorithms-Random Forest (RF), Support Vector Machines (SVM), Decision Tree (DT), and Logistic Regression (LR). To reduce the bias data shuffling was performed initially to randomize the order of tuples. Categorical variables changed to numerical variables with use of factorization for accurate algorithmic processing. Feature selection techniques like Mean Decrease in Impurity (MDI), Recursive Feature Elimination (RFE), and Random Forest Mean Decrease (RFMD) were used to reduce computation load without compromising performance. The study deeply evaluates model performance on the basis of sensitivity, specificity, accuracy, and detection rate. Among the algorithms, RF demonstrated superior accuracy when combined with the selected features. Visualizations using importance scores and confusion matrices provided deeper insights into the models, which aids in interpreting the findings. Finally, an app was developed with selected 8 features and RF to predict the lung cancer. This research demonstrates the vital role of ML in lung cancer diagnosis and its potential of early detection and treatment. The findings of research contributed in advancing the healthcare and addresses the global challenges of lung cancer. © 2024 IEEE.

关键词： Lung cancer

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Influence of AI and MAS in Enhancing Energy Efficiency of 6G Communications

Influence of AI and MAS in Enhancing Energy Efficiency of 6G...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Negi, Divya Prakash, Febin Gupta, Anjali Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Electronical And Electronics Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350366846

Next-generation Internet of Things (IoT) is progressing rapidly due to the introduction of beyond 5G (B5G) and the approaching arrival of 6G, which have improved the dependability, productivity, and profitability of both industrial and personal operations. Nevertheless, data overload is frequently caused by the abundance of wireless sensors in commercial 6G devices. Data mining gathers useful information in order to solve this. Meanwhile, in the 6G architecture, edge computing enables automated equipment operations and intelligent decision-making in real-time. Even so, despite the drawbacks of sensor-based energy sources, the integration of many technologies has led to an increase in energy. Using a multi-agent system (MAS) approach, a system model for industrial wireless sensor networks in 6G applications has been presented in order to combat this. Sensor node clustering locates and forecasts the locations of principal nodes by employing distributed artificial intelligence (DAI). Convolutional neural networks (CNN) and back-propagation neural networks (BPNN) are used in optimisation. Allocating resources to individual nodes efficiently is made possible by the analysis of inter-cluster correlations. The outcomes of the simulation show how well the approach works to reduce resource waste, increase overall energy efficiency, and protect important data. This strategy improves network performance in industrial 6G applications while simultaneously addressing concerns about energy consumption. © 2024 IEEE.

关键词： Multi agent systems

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The Firstever LTE Network on the Moon: Challenges and Prospects 5

The Firstever LTE Network on the Moon: Challenges and Prospe...

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5th IEEE International Conference on Recent Trends in computer Science and technology, ICRTCST 2024

作者： Routray, Sudhir K. Pappa, M. Singh, Monika Javali, Abhishek Sharmila, K.P. Tengshe, Richa CMR University Department of Computer Science and Engineering Bangalore India CMR Institute of Technology Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798350351378

Mobile cellular networks are very popular across the globe, and they are found at all places where human settlements are present. Long term evolution (LTE) is a popular standard for mobile wireless communications. It has been deployed in almost all the countries. These networks are deployed to support various services, such as voice, image, video, text, and data transmission. In 2024, NASA and Nokia together get ready to deploy an LTE network on the moon. This project was planned in 2020, and NASA selected Nokia as the vendor for this lunar LTE network deployment mission. The LTE network on the moon would help in the enhancement of lunar exploration. The common services of LTE are planned to be deployed on the lunar surface. In addition to that, some moon specific valueadded services will also be deployed to support the exploration tasks. The deployed network would send the pictures, videos, and other sensor measured data from the lunar surface back to earth. This lunar network will be supported by the orbiting satellites and other peripheral networks. In this paper, the basic features and characteristics of the first lunar LTE network and its utilities have been studied. © 2024 IEEE.

关键词： NASA

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