A key method used in the study of Natural language Processing (NLP) is sentiment analysis, or emotion analysis, plays a pivotal role in text analysis. Its primary function is to discern and categorize the underlying e...
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Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerab...
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Tropical cyclones, characterized by strong winds and heavy rainfall, threaten human life in coastal regions crucial to the economy, including fisheries, agriculture, tourism, and infrastructure. Their frequent occurre...
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ISBN:
(数字)9798331528171
ISBN:
(纸本)9798331528188
Tropical cyclones, characterized by strong winds and heavy rainfall, threaten human life in coastal regions crucial to the economy, including fisheries, agriculture, tourism, and infrastructure. Their frequent occurrence impacts communities reliant on these industries for livelihoods. Accurate estimation of tropical cyclone intensity is vital for disaster preparedness, risk assessment, and timely evacuations. Recent advancements in machine learning and deep learning have been applied to predict cyclone intensity from satellite images, providing insights into cyclone dynamics and enhancing disaster response. This paper analyzes recent research on intensity estimation using various machine learning algorithms and discusses future prospects for improving accuracy and reliability.
Lip Reading AI is a discipline that is rapidly changing and has numerous applications in security, accessibility and human-computer interaction. This paper proposes a model which combines Convolutional Neural Networks...
Lip Reading AI is a discipline that is rapidly changing and has numerous applications in security, accessibility and human-computer interaction. This paper proposes a model which combines Convolutional Neural Networks (CNNs) to capture spatial capabilities, Long Short-Term Memory (LSTM) networks to examine temporal dependencies, and an adaptive interest mechanism. Meticulous preprocessing of the MIRACL VC-l dataset addressing challenges including one of a kind lip moves and occlusions accompanied with the aid of transitioning this study effortlessly to LRS2 dataset to complement lexemic versatility is one of its key function. The effects verify its robustness throughout unique datasets with superior overall performance towards cutting-edge techniques. Ablation checks suggest the crucial significance of every element in phrases of improving lip analyzing accuracy. Our proposed model version additionally suggests flexibility in restricted and naturalistic language situations.
Lung cancer impacts both genders, and its early detection is key to lowering death rates. Current deep learning techniques for automated identification and classification of lung carcinoma face issues with interpretab...
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Fruit and Vegetable Recognition with Calorie Estimation based on Mobilenetv2 is a pioneering research endeavor aimed at leveraging deep learning techniques to enhance dietary monitoring and health management. Building...
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ISBN:
(数字)9798350383867
ISBN:
(纸本)9798350383874
Fruit and Vegetable Recognition with Calorie Estimation based on Mobilenetv2 is a pioneering research endeavor aimed at leveraging deep learning techniques to enhance dietary monitoring and health management. Building upon the success of neural network models in various domains, this study explores the application of Mobilenetv2 and EfficientNet architecture for accurately identifying fruits and vegetables from images and estimating their respective caloric content. The research dataset comprises meticulously curated images of diverse fruits and vegetables, ensuring comprehensive coverage across different categories. Through rigorous experimentation and evaluation, the proposed model demonstrates remarkable accuracy in fruit and vegetable recognition, achieving an impressive accuracy rate of 97.6%. Moreover, the incorporation of calorie estimation adds a novel dimension to dietary analysis, enabling users to make informed decisions regarding their nutritional intake. The findings of this research not only contribute to the advancement of computer vision techniques but also hold significant implications for personalized nutrition tracking and health- conscious applications.
The technological advancements in the field of agriculture have increased to a great extent in recent years, and many techniques have evolved from other techniques. Some methods are improved or upgraded from the previ...
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Building trust between consumers and service providers is critical to the functioning of cloud computing, a technology that offers a range of services over the Internet. When consumers choose which cloud services to u...
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This paper focuses on self-healing algorithms in structural health monitoring (SHM) systems centered around the enhancement of resilience and adaptability of the systems. In this study, imports from existing methods (...
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ISBN:
(数字)9798331515683
ISBN:
(纸本)9798331515690
This paper focuses on self-healing algorithms in structural health monitoring (SHM) systems centered around the enhancement of resilience and adaptability of the systems. In this study, imports from existing methods (clustering, Fault Tolerant Multiple Redundancy (FTMR) and reinforcement learning) are analyzed against the choice of creating a novel retasking algorithm designed for dynamic resource redistribution and optimal monitoring coverage. Unlike conventional methods, retasking will allow adapting the coverage in real time, whereby system down time will be reduced, with less computational load achieved through task redistribution through functional sensors. Findings showed that retasking improved reliability and scalability of the SHM systems drastically, providing a simple yet powerful resolution towards modern infrastructure monitoring. This study stresses the retasking capability to redefine self-healing in the SHM systems for future directions in infrastructure safety.
Early and accurate detection of anomalous events on the freeway, such as accidents, can improve emergency response and clearance. However, existing delays and errors in event identification and reporting make it a dif...
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