This work investigates using Convolutional Neural Networks (CNN) and VGGNet architecture, for realtime emotion detection. Raw data from the FER-2013 dataset is transformed from CSV format into organized image director...
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ISBN:
(数字)9798350372120
ISBN:
(纸本)9798350372137
This work investigates using Convolutional Neural Networks (CNN) and VGGNet architecture, for realtime emotion detection. Raw data from the FER-2013 dataset is transformed from CSV format into organized image directories, segmented by distinct emotions. The resulting model, equipped with convolutional, pooling, and dense layers, effectively classifies emotions from 48x48 grayscale images. In application, the model overlays detected emotions onto a webcam stream. An integrated music player module synchronizes music with the subject’s emotions. Overall, this research explores a comprehensive interactive system focused on recognizing and responding to human emotions. Without further training data, our model, to the best of our understanding, achieves modern single-network accuracy of 74.57% on FER2013
Industrial Internet of Things (IIoTs) comprises large-scale sensors deployed to monitor and control the system. Deploying IoT devices presents challenges due to frequent disconnections and sudden power drainage in int...
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Microplastic pollution poses a significant environmental threat, necessitating advanced methods for effective control and mitigation. This paper focuses on the development of a novel approach for micro-objects classif...
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ISBN:
(数字)9798350389449
ISBN:
(纸本)9798350389456
Microplastic pollution poses a significant environmental threat, necessitating advanced methods for effective control and mitigation. This paper focuses on the development of a novel approach for micro-objects classification, specifically targeting microplastics, utilizing holographic images. Holography provides a three-dimensional representation of microscopic particles, enhancing the accuracy and efficiency of classification algorithms. In this paper, initially the acquisition of holographic images of water samples containing micro-objects, with a particular emphasis on microplastics are collected. State-of-the-art image processing and machine learning techniques are employed to develop a robust classification system capable of distinguishing between various micro-objects with high precision. Next, the Key objectives include the design and implementation of classification algorithm, leveraging holographic data to differentiate microplastics from other particles. The proposed work aims to enhance the efficiency of microplastic identification, enabling more effective monitoring and control of pollution in aquatic *** outcomes of this paper hold promise for advancing microplastic pollution research and environmental monitoring practices. The integration of holographic images into the classification process offers a unique perspective, providing valuable insights into the characteristics and distribution of microplastics. Ultimately, this research contributes to the development of innovative solutions for addressing the pervasive issue of microplastic pollution.
The traditional Radio-Frequency systems (RFS) authentication methods, designed to ensure secure data transmission on the web, may not always effectively prevent adversaries from gaining access to concealed IDs or asym...
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Trustworthy machine learning aims at combating distributional uncertainties in training data distributions compared to population distributions. Typical treatment frameworks include the Bayesian approach, (min-max) di...
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Stochastic iterative algorithms, including stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD), are widely utilized for optimization and sampling in large-scale and high-dimensional prob...
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This paper presents the experimental evaluation of an AI-powered vehicle toll collection system, utilizing RFID technology and IoT integration. The system was designed to automate toll collection, reducing transaction...
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Authentication is a crucial step in the cyber security process that confirms user identities. Even though they are widely used, traditional password based techniques are frequently vulnerable to attacks like guessing ...
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India, whose population growth is its greatest asset cannot afford to let its people eat confectionery that is either tainted or somehow polluted, since this would lead to extensive malnutrition. Traditional food supp...
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ISBN:
(数字)9798331543624
ISBN:
(纸本)9798331543631
India, whose population growth is its greatest asset cannot afford to let its people eat confectionery that is either tainted or somehow polluted, since this would lead to extensive malnutrition. Traditional food supply chains are characterized by centralization and the problems associated with it such as having a single point of failure and producing inconsistent products while compromising the quality, along with data loss. With the need for daily reports in all regions of India regarding food fraud, contamination, and adulteration, a clearly advanced decentralized model is sought after for the creation of a supply chain. This paper introduces the supply chain network for the Indian confectionery market based on blockchain to better address quality claims, contamination, and transparency. This enables real-time traceability within the ecosystem, by using IoT strategies along with the Ethereum blockchain leading to better product authentication and resulting in higher consumer confidence. Blockchain creates a secure data tracking function, while smart contracts can be used to define the process - such as IoT monitoring of temperature. The united method enables verified data to be available to the consumer, energy efficiency, and trust across the supply chain.
Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causi...
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