The primary goal of any company is to increase its profits by improving both the quality of its products and how they are advertised. In this context, neuromarketing seeks to enhance the promotion of products and gene...
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Understanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 pat...
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Sonic interaction design is defined as the study and exploitation of sound as one of the principal channels conveying information, meaning, and aesthetic/emotional qualities in interactive contexts. This field lies at...
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ISBN:
(数字)9798331514846
ISBN:
(纸本)9798331525637
Sonic interaction design is defined as the study and exploitation of sound as one of the principal channels conveying information, meaning, and aesthetic/emotional qualities in interactive contexts. This field lies at the intersection of interaction design, and sound and music *** the virtual reality community, the focus of research on topics related to auditory feedback has been rather limited when compared, for example, to the focus placed on visual feedback or even on haptic feedback. However, in such communities as film production or product sound design it is well known that sound is a powerful way to communicate meaning and emotion to a scene or a *** 2025 is the 8th of a series of workshops, whose main goal is to increase awareness among the virtual reality community of the importance of sonic elements when designing virtual/augmented/mixed reality (XR hereafter) environments. Participants to the workshop will also discuss how research in other related fields such as film sound theory, product sound design, sound and music computing, game sound design, and accessibility can inform designers of XR environments. Moreover, the workshop will feature state-of-the-art research in the field of sound for XR environments.
Background: Vehicular Ad Hoc Networks (VANETs) play a crucial role in intelligent transportation by facilitating communication between vehicles and vehicles with infrastructure-based models. They encounter problems su...
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ISBN:
(数字)9798331508456
ISBN:
(纸本)9798331508463
Background: Vehicular Ad Hoc Networks (VANETs) play a crucial role in intelligent transportation by facilitating communication between vehicles and vehicles with infrastructure-based models. They encounter problems such as significant movement, overcrowding, and loss of data. This research presents a novel deep reinforcement learning-based resource allocation and congestion optimization (DRLRCO) framework aimed at improving communication efficiency and high reliability using an effective learning process. Method Used: The DRLRCO framework employs agent-based learning to ensure precise data transmission, AdMAC Protocol for dynamic packet delivery, and Congestion-Aware Chicken Swarm Optimization (CSO) to enhance network performance. Result: The DRLRCO framework surpasses reinforcement learning models for comprehensive testing. Significant enhancements involve improved accuracy, reduced data loss, minimized overhead, increased throughput, and shorter average delays for real-time communication. Conclusion: The DRLRCO framework successfully tackles congestion and data loss in vehicular communication. Future studies might involve Artificial Intelligence (AI) for traffic forecasting, experimentation among high-speed vehicles and investigating blockchain for enhanced security.
Permaculture is a land management and regenerative agriculture that is the integration of technological advancement and the natural agricultural ecosystems. It is usually manifested in green architecture and balcony g...
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Compared to natural images, hyperspectral images (HSIs) consist of a large number of bands, with each band capturing different spectral information from a certain wavelength, even some beyond the visible spectrum. The...
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Forty years ago, in 1983, Lee Schruben proposed the Event Graph formalism and modeling language, subsequently defining the paradigm of Event-Based Simulation, in a precise way, which had been pioneered 20 years before...
Forty years ago, in 1983, Lee Schruben proposed the Event Graph formalism and modeling language, subsequently defining the paradigm of Event-Based Simulation, in a precise way, which had been pioneered 20 years before by SIMSCRIPT. The purpose of this panel is for a group of Event Graph researchers both from Operations Research and computer Science, including the inventor of Event Graphs and one of his former PhD students who has made essential contributions to their theory, to discuss their views on the history and potential of Event Graph modeling and simulation. In particular, the adoption of Event Graphs as a discrete process modeling language in Discrete Event Simulation and in computer Science, and their potential as a foundation for the entire field of Discrete Event Simulation and for the fields of process modeling and AI in computer Science is debated.
One of the most crucial issues that smart cities have to keep addressing as they grow is urban mobility improvement. Maximizing the efficiency of transportation, so lowering congestion, and so optimizing traffic flow ...
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ISBN:
(数字)9798331541217
ISBN:
(纸本)9798331541224
One of the most crucial issues that smart cities have to keep addressing as they grow is urban mobility improvement. Maximizing the efficiency of transportation, so lowering congestion, and so optimizing traffic flow can help to solve the growing urbanization and increasing vehicle numbers. This research aims to develop and implement a deep learning method based on DenseNet in order to reach the target of raising the effectiveness of urban traffic management. The unique selling proposition that distinguishes this sector from others is the introduction of modern technologies into a sector that has always depended on conventional methods. The main goal of the work was to apply DenseNet architecture to analyze traffic patterns. Considering the two other strategies, the proposed option has a lot of possibilities. From evaluation criteria-which comprised recall, precision, and F1-score, the proposed solution outperformed the approaches regarded to be state-of-the-art.
Facial expression recognition is one of the fields that nowadays has attracted the attention of many researchers. It is possible to automate facial expression recognition using artificial intelligence methods. This wi...
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ISBN:
(纸本)9798350398830
Facial expression recognition is one of the fields that nowadays has attracted the attention of many researchers. It is possible to automate facial expression recognition using artificial intelligence methods. This will be of great help to researchers, especially in areas such as psychology. Automatic facial recognition can be derived from a static image of facial expression, but a better and more efficient way to do this is through a sequence of images. In this paper, a new method is proposed to automatically detect facial expressions from a sequence of images. Each sequence of facial images begins with a face neutral state and ends with one of the six main emotions. Motion vectors are extracted from the sequence using optical flow algorithm. These vectors are then used to train the conditional random field and finally to automatically determine the emotion. In this paper, in addition to the basic conditional random field, the hidden dynamic conditional random field is also investigated. Additionally, the effect of changing some parameters of these algorithms such as different optimization methods has been investigated. Given that a facial expression is recognized during a sequence of images, random field-based methods can be used for efficient classification of facial expressions reaching accuracy (more than 90%) competitive with the best existing methods for facial expression recognition.
Conventional methods for diagnosing bridge damage have centred on tracking changes in modal-based Damage Sensitive Features (DSFs), which are strongly related to structural stiffness. Environmental and operational var...
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ISBN:
(数字)9798350374957
ISBN:
(纸本)9798350374964
Conventional methods for diagnosing bridge damage have centred on tracking changes in modal-based Damage Sensitive Features (DSFs), which are strongly related to structural stiffness. Environmental and operational variations, inefficient use of machine learning approaches for damage detection, and an overall reliance on modal-based DSFs are some of the limits and hurdles that the system still faces, despite substantial progress towards practical deployment. According to the proposed method, the initial three steps are data preparation, feature extraction, and model training. In order to identify damage during the preprocessing step, the suggested approach utilises anomaly scores. Features in the timefrequency domain allow for parallel processing of signals in the signal-to-feature extraction pipeline. After the features have been extracted, the data is used to train GBDT-BiLSTM models. The proposed approach achieves better results than the wellknown alternatives, GBDT and BiLSTM.
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