Soil nutrients have a significant contribution in the health of soil and in productivity of crops. However, traditional methods of analysis of different parameters of soil such as electrical conductivity (EC), pH, org...
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As wine tops as one of the most consumed beverages in the world, there have been several studies on improving wine quality over the years. Our paper aims to enhance the predictive accuracy of wine quality certificatio...
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Breakthroughs in machinelearning (ML) and advances in quantum computing (QC) drive the interdisciplinary field of quantum machinelearning to new levels. However, due to the susceptibility of ML models to adversarial...
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ISBN:
(纸本)9798331541378
Breakthroughs in machinelearning (ML) and advances in quantum computing (QC) drive the interdisciplinary field of quantum machinelearning to new levels. However, due to the susceptibility of ML models to adversarial attacks, practical use raises safety-critical concerns. Existing Randomized Smoothing (RS) certification methods for classical machinelearning models are computationally intensive. In this paper, we propose the combination of QC and the concept of discrete randomized smoothing to speed up the stochastic certification of ML models for discrete data. We show how to encode all the perturbations of the input binary data in superposition and use Quantum Amplitude Estimation (QAE) to obtain a quadratic reduction in the number of calls to the model that are required compared to traditional randomized smoothing techniques. In addition, we propose a new binary threat model to allow for an extensive evaluation of our approach on images, graphs, and text.
Sentiment analysis is a vital aspect of word processing that is becoming increasingly successful with the advent of machinelearning. This article provides a comparative evaluation of machinelearning-based analytics ...
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Finding the top candidates for a position is the aim of the resume screening process. The application must make use of machinelearning methodologies as well as natural language processing to rate candidates in real t...
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The emergence of Agriculture 4.0 signifies a transformative shift in traditional farming practices, integrating cutting-edge technologies like IoT, machinelearning, and robotics to support productivity and sustainabi...
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Facilities Management (FM) companies rely on effective and low cost data collection from Appliance Load Monitoring (ALM) devices to provide asset quality and energy monitoring services. The introduction of an automate...
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ISBN:
(纸本)9798350365924;9798350365917
Facilities Management (FM) companies rely on effective and low cost data collection from Appliance Load Monitoring (ALM) devices to provide asset quality and energy monitoring services. The introduction of an automated appliance type classification pipeline during installation and inspection can offer huge improvements in reducing cost and installation errors. Most work focus on showcasing Voltage-Current (V-I) trajectory features based machinelearning (ML) and Deep learning (DL) algorithms on benchmarking datasets rather than providing mechanisms for deploying their model onto a production-ready system. This paper introduces a feature extraction preprocessing approach for ensuring the validity of detected steady-state events in VI trajectories that can be used with machinelearning (ML) models to identify FM asset types during site installations of Appliance Load Monitoring (ALM) units. We introduce a framework in which the approach can be used as part of the training and deployment of ML models for verifying and monitoring assets in FM client environments.
The agricultural area has undergone a significant transformation owing to the progress made in IoT. It is imperative to have a dependable remote monitoring solution right now. This study aims to accomplish two goals. ...
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Speech emotion recognition (SER) is a pivotal component of human-computer interaction. This research attempts to utilize Mel Frequency Cepstral Coefficients (MFCC) to devise a transfer learning-like approach. Using a ...
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Artificial Intelligence (AI) & machinelearning (ML) stands as a robust technology empowering cybersecurity team with the automation of repetitive tasks, expediting threat detection and response, and enhancing the...
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