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共 7041 条记录，以下是161-170 订阅

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Classification of.Hashtag’s Popularity Growth Patterns Using Shape Dynamic Time Warping

Classification of Hashtag’s Popularity Growth Patterns Usin...

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2024 IEEE International Conference on Data and Sof.ware engineering, ICoDSE 2024

作者： Hayashi, Ren Hashimoto, Yasuhiro Dept. Information Technologies and Project Management The University of Aizu Aizu-Wakamatsu Japan Dept. Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan

ISBN: (纸本)9798331506407

This study presents a new method for classifying growth patterns of.cultural elements, specifically focusing on hashtag popularity in social media. We use the Yule-Simon process as a baseline and apply Shape Dynamic Time Warping (ShapeDTW) to categorize hashtags into distinct growth patterns. Our approach successfully identified typical growth patterns by setting similarity thresholds. Notably, our linear and logarithmic scale comparison revealed that the latter was more effective in identifying characteristic growth patterns. This was particularly evident for time series demonstrating Preferential Attachment-like mechanisms. Our findings highlight the importance of.considering both scale and underlying growth mechanisms when analyzing time series of.cultural phenomena. © 2024 IEEE.

关键词： Time series

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Design of.a Collaborative Energy-Aware Routing Protocol for UAV-VANETs

Design of a Collaborative Energy-Aware Routing Protocol for ...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Hsu, Tz-Heng Wang, Zhi-Hao Horng, Gwo-Jiun Southern Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Tainan Taiwan Southern Taiwan University of Science and Technology Dept. of Information Management Tainan Taiwan

ISBN: (纸本)9798350324174

Due to the mobility of.drones and their limited power supply, the network topology frequently changes, making routing more complex. To address this issue, an energy-aware routing protocol is proposed to extend the lifespan of.the unmanned aerial vehicle (UAV). Because vehicles have less power constraints, the proposed algorithm prioritizes vehicles for packet transmission when multiple paths exist, and both UAV networks and vehicular communication networks can aid in packet forwarding to improve the lifespan of.UAV networks. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Mask Generation with Meta-Learning Classifier Weight Transformer Network for Few-Shot Image Segmentation

Mask Generation with Meta-Learning Classifier Weight Transfo...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Jhou, Fong-Ci Liang, Kai-Wen Lo, Chung-Hsun Wang, Chien-Yao Chen, Yung-Fang Wang, Jia-Ching Chang, Pao-Chi National Central University Dept. Communication Engineering Taiwan National Central University Dept. Computer Science and Information Engineering Taiwan Academia Sinica Institute of Information Science Taipei Taiwan

ISBN: (纸本)9798350324174

This paper proposes a meta-learning classification weight transfer network to generate masks as a few-shot image segmentation architecture. It generates good prior masks via a pretrained classification weight transfer architecture, and uses a pretrained feature extraction architecture on query images and support images. The network architecture exploits a top-down path in a feature augmentation module to adaptively transfer information from fine to coarse features for extracting features from query images. Finally, the classification module predicts the segmentation of.the query image. The experimental results show that using the mean intersection of.joints (mIOU) as the evaluation mechanism, the accuracy of.the 1-shot experimental results is 1.7% higher than that of.the baseline. In the 5-shot experimental results, the accuracy is also improved by 2.6%. Therefore, compared with the baseline, it clearly shows that the mask generated by the meta-learning classification weight transfer network can effectively help improve the performance of.few-shot image segmentation system. © 2023 IEEE.

关键词： Semantic Segmentation

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An Energy Recharging Scheme in Mobile Wireless Sensor Networks

An Energy Recharging Scheme in Mobile Wireless Sensor Networ...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Hu, ZhiJie Chang, Chih-Yung Shen, RuiBing Wu, Shih-Jung Xi'an Eurasia University School of Information Engineering Xi'an China Tamkang University Dept. of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

With the development of.the Internet of.Things, applications of.the wireless sensor network are more and more widespread. In wireless sensor networks, the sensor is basically stationary and performs sensing tasks at the deployed location. The mobile charger moves to the sensors for recharging them. However, the energy of.the mobile charger is limited. It usually consumes lots of.energy for moving and recharging. As a result, some sensors die due to the lack of.timely recharged, which affects the monitoring quality and recharging efficiency. In this paper, the sensors are mobile. They can move to the recharging station in order to be recharged by the mobile charger, which reduces the cost of.the mobile charger's traversal. At the same time, the proposed algorithm considers the cooperation of.multiple mobile chargers, aiming to shorten the recharging cycle. In addition, the sensors also locally adjust their sensing frequencies for improving the sensing data quality according to the starting recharging time. Experimental results show that the proposed recharging algorithm improves the sensing quality compared to the existing algorithms. © 2023 IEEE.

关键词： Wireless sensor networks

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WellBe: An Intelligent Elderly Care and Well-Being Monitoring System Using Deep Learning 4

WellBe: An Intelligent Elderly Care and Well-Being Monitorin...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Raotole, Atharv Shirodkar, Sailee Sanjay Shukla, Rahul Sisodia, Jignesh Devadkar, Kailas Patel Institute of Technology Dept. of Information Technology Sardar Mumbai India Patel Institute of Technology Dept. of Computer Science and Engineering Sardar Mumbai India

ISBN: (纸本)9798350349900

Deep Learning refers to a subset of.Machine Learning that utilizes deep neural networks to simulate the complex decision-making process of.the human brain. In recent years, DL has made remarkable progress in addressing multiple unique challenges faced by most elderly individuals. While there have been significant contributions to independent systems catered for the elderly, there is the existence of.an overall integrated AI platform that unifies the use of.individual modules for an optimized solution. This paper introduces 'WellBe,' an innovative AI system that encompasses an alarm-based fall detection component, a facial emotion recognition (FER) system and a built-in personalized chatbot all under a user-friendly website. The novelty of.the work extends beyond the aforementioned components, owing to the implementation of.an optimized FER system able to handle wrinkles and changes in skin texture, thereby tailored exclusively for the elderly demographic. The system leverages a custom feature extraction module, fusing facial and wrinkle features, in addition to extending a convolutional neural network (CNN) architecture for accurate emotion classification, achieving promising performance with precision, recall, and F1-scores of.0.87, 0.91, and 0.89 for the 'Happy' (one of.the predicted emotions) class, respectively. The fall detection component, on the other hand, utilizes a pre-trained YOLOv7 Pose model for the real-time identification of.falls, sitting at a performance accuracy of.95.3%. Finally, the personalized chatbot is implemented using a rule-based approach enhanced with the Jaccard similarity algorithm, to facilitate prompt assistance and a sense of.companionship to the end user. Overall, the integration of.these components into a cohesive website of.ers a comprehensive solution for enhancing the quality of.life for elderly individuals living alone, addressing loneliness, safety, and overall well-being. © 2024 IEEE.

关键词： Convolutional neural networks

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Leveraging Network Vulnerability Detection using Improved Import Vector Machine and Cuckoo Search based Grey Wolf Optimizer 1

Leveraging Network Vulnerability Detection using Improved Im...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Preethi, P. Vasudevan, I. Saravanan, S. Krishna Prakash, R. Devendhiran, A. Kongunadu College of Engineering and Technology Dept. of Computer Science and Engineering Trichy India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India Kl University Dept. of Computer Science and Engineering Vijayawada India Sona College of Technology Dept. of Information Technology Salem India Vivekanandha College of Engineering for Women Dept. of Computer Science and Technology Namakkal India

ISBN: (纸本)9798350328042

In the current interconnected and susceptible digital environment, the demand for reliable network attack prediction systems has reached a critical level. This research paper presents a novel approach for network intrusion detection utilizing the Improved Import Vector Machine (IVM) classification algorithm combined with feature selection using Cuckoo Search based Grey Wolf Optimization (CS-GWO). The aim is to improve the accuracy and effectiveness of.intrusion detection systems by selecting the most relevant features and employing a powerful classification algorithm. The proposed methodology begins with data preprocessing, ensuring the dataset is appropriately cleaned and normalized. Subsequently, the CS-GWO algorithm is applied for feature selection, leveraging the strengths of.both Cuckoo Search and Grey Wolf Optimization. This hybrid approach efficiently explores the search space to identify the optimal feature subset that contributes to accurate intrusion detection. Once the feature selection process is completed, the IVM classification algorithm is employed to classify network instances into intrusion or normal categories. The IVM algorithm is chosen for its ability to handle high-dimensional data and its robustness in dealing with imbalanced datasets commonly encountered in network intrusion detection. To evaluate the performance of.the proposed approach, extensive experiments are conducted using benchmark datasets. Various evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of.the system in detecting network intrusions. The results demonstrate that the combination of.IVM classification and CS-GWO feature selection significantly improves the accuracy and efficiency of.network intrusion detection. The selected features obtained through CS-GWO contribute to enhanced classification performance, enabling the system to accurately identify and respond to potential intrusions in real-time scenarios. © 2023 IE

关键词： Network security

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Enhancing Early Detection of.Pancreatic Cancer: A Machine Learning Approach with Explainable AI Insights

Enhancing Early Detection of Pancreatic Cancer: A Machine Le...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Pattanashetti, Neeraj V Mayur, R. Prakash, Adarsh Adiga, Tryambaka B Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bangalore India Global Academy of Technology Dept. of Information Science & Engineering Karnataka Bangalore India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India

ISBN: (纸本)9798350376876

Among the most deadly and complex illnesses in the world, pancreatic cancer is typified by its early asymptomatic stage and subsequent discovery of.the disease when it has progressed to an advanced stage. Getting the patients in a correct treatment path at the earliest point is important for suitable intervention and patient outcomes in the long run. We utilized advanced algorithms and model prediction techniques includes, feature extraction, hyperparameter tuning and explainability methods which aims to enhance both the comprehension and predictive accuracy of.models employed. The goal is to assess patient health status and diagnose pancreatic diseases (cancer vs. non-cancer) by developing a learning algorithm. We obtained a dataset containing demographic information and urine biomarkers to start the investigation. First stage of.our analysis employs a single classifier and later we apply bagging and boosting algorithms to achieve ensemble models. Hyperparameter fine-tuning has got a positive outcome, which has Gradient Boosting and Cat Boosting as its top performing models. The ensemble model produced excellent results (92.02 %), demonstrating the usefulness of.the suggested approach. LIME and SHAP techniques play the key role in model outcome interpretability providing an insight of.algorithmic decision-making process. Such a wide range of.combinations of.biomarkers have a potential of.pancreatic cancer detection in early stages and thus will help shape diagnostics strategies in near future. © 2024 IEEE.

关键词： Diseases

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Muscle-Guided Guitar Pedalboard: Exploring Interaction Strategies Through Surface Electromyography and Deep Learning

Muscle-Guided Guitar Pedalboard: Exploring Interaction Strat...

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International Conference on New Interfaces for Musical Expression, NIME 2024

作者： Lionetti, Davide Belluco, Paolo Zanoni, Massimiliano Turchet, Luca Dept. of Art and Science University of Ghent Ghent Belgium LWT3 Milan Italy Dept. of Electronics Information and Bioengineering Polytechnic University of Milan Milan Italy Dept. of Information Engineering and Computer Science University of Trento Trento Italy

This paper explores a method to innovate the conventional interaction with a guitar pedalboard. By analyzing muscular contractions tracked via surface Electromyography (sEMG) wearable sensors, we aimed to investigate how to dynamically track guitarists’ sonic intentions to automatically control the guitar sound. Two Recurrent Neural Networks based on Bidirectional Long-Short Term Memory were developed to analyze sEMG signals in real-time. The system was designed as a digital musical instrument that calibrates itself to each user during an initial training process. During training musicians provide their gestural vocabulary, associating each gesture to a corresponding pedalboard preset. The selection of.the most effective features, in synergy with the best set of.muscles, was conducted to optimize the learning rate of.the system. The system was assessed with a user study encompassing seven expert guitar players. Results showed that, on average, participants appreciated the concept underlying the system and deemed it to be able to foster their creativity. © 2024, International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： Long short-term memory

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Sparse Channel Estimation in Massive MIMO Systems Using Compressed Sensing and Neural Networks

Sparse Channel Estimation in Massive MIMO Systems Using Comp...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

In this paper, based on compressed sensing and neural networks, we present an intelligent sparse channel estimation technique for Massive MIMO systems. We show that our approach improves considerably the estimation accuracy in terms of.Mean Squared Error (MSE) and Normalized Mean Squared Error (NMSE) with respect to classical methods. We demonstrate [Figure: ] that our approach is computationally more efficient, requires less time and resources (memory/disk) compared to the existing literature but for a robustness of.performance across distal or low Signal-to-Noise Ratios (SNRs). We also confirm our method exhibits fast convergence and efficiently accommodates a variety of.sparsity levels. By visualizing it using heatmaps and ROC curves we show that this method is able to effectively differentiate between signal and noise, asserting our approach as a powerful tool for the contemporary set of.problems present in wireless communications. © 2024 IEEE.

关键词： Signal to noise ratio

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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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