This paper evaluates the efficiency of eleven outlier detection algorithms applied to three agricultural IoT datasets: Weather in Szeged, Greenhouse, and Crop Recommendation. It focuses on how data quality affects IoT...
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ISBN:
(数字)9798331532970
ISBN:
(纸本)9798331532987
This paper evaluates the efficiency of eleven outlier detection algorithms applied to three agricultural IoT datasets: Weather in Szeged, Greenhouse, and Crop Recommendation. It focuses on how data quality affects IoT systems' reliability in agriculture. The analyzed algorithms include statistical, distance-based, density-based, ensemble, and hybrid methods, and their ability to detect outliers is investigated using recall, precision, and F1-score. The results demonstrate that algorithm performance is influenced by dataset characteristics such as dimensionality, nonlinearity, and temporal dependencies. Isolation Forest (iForest) consistently outperformed other algorithms due to its non-parametric design and recursive partitioning. In contrast, algorithms like Cluster-Based Local Outlier Factor (CBLOF), K-Nearest Neighbors (KNN), and Gaussian Mixture Model (GMM) showed variable effectiveness depending on dataset features. The study emphasizes the need for selecting the appropriate outlier detection method tailored to specific agricultural datasets and recommends future research on hybrid models or deep learning approaches to enhance anomaly detection in agricultural IoT systems.
In recent years, research has focused on the efficacy of neural networks, Learning by Transfer (TL), and Ensemble Learning (EL) techniques in image processing. Ensemble approaches, when employed on image classificatio...
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Serverless development is challenging as applications are composed of stateless and short-lived functions. Many workflows require time-bound functions to transfer their state to other function before termination. The ...
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One of the most important aspects of any network, whether wired or wireless, is keeping the transmitter and receiver’s time in sync. In a mobile ad hoc network, each node is constantly adapting to its immediate surro...
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In the field of automated language processing, distinguishing between Moroccan Arabic (Darija) in multilingual contexts is a major challenge. This study addresses this challenge by exploiting feature selection techniq...
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Two common methods for interpreting machine learning models are counterfactual explanations and attributional explanations, each with its own advantages and limitations. Attributional interpretation assigns an importa...
Two common methods for interpreting machine learning models are counterfactual explanations and attributional explanations, each with its own advantages and limitations. Attributional interpretation assigns an importance score to each input feature, but it is difficult to ensure fidelity when interpreting complex models, and the commonly used attributional interpretations LIME and SHAP are based on the sufficiency definition. Counterfactual explanations provide minimally varying input instances to change model predictions, but are less likely to reflect generalizability, and their generated instances can be related to the necessity definition. To integrate these methods and leverage their respective advantages, we propose a novel approach for generating feature attribution explanations using counterfactual instances. We construct sufficiency impact terms and necessity impact terms from the generated counterfactual instances, and aggregate these terms by weight to derive feature importance scores. We evaluate it on three public datasets Adult-Income, Lending-Club, and German-Credit. The experimental results demonstrate that our method places greater emphasis on feature adequacy and necessity, and is more faithful to the original machine learning model.
Race classification is a long-standing challenge in the field of face image *** investigation of salient facial features is an important task to avoid processing all face *** segmentation strongly benefits several fac...
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Race classification is a long-standing challenge in the field of face image *** investigation of salient facial features is an important task to avoid processing all face *** segmentation strongly benefits several face analysis tasks,including ethnicity and race *** propose a race-classification algorithm using a prior face segmentation framework.A deep convolutional neural network(DCNN)was used to construct a face segmentation *** training the DCNN,we label face images according to seven different classes,that is,nose,skin,hair,eyes,brows,back,and *** DCNN model developed in the first phase was used to create segmentation *** probabilistic classification method is used,and probability maps(PMs)are created for each semantic *** investigated five salient facial features from among seven that help in race *** are extracted from the PMs of five classes,and a new model is trained based on the *** assessed the performance of the proposed race classification method on four standard face datasets,reporting superior results compared with previous studies.
Food safety is an important issue nowadays. Now, people become more aware of food safety and health concerns as their lifestyles have changed they follow more precautions for food consumption. The conventional techniq...
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A new approach to cancer care, precision medicine tailors treatments to each patient's specific genetic, molecular, and clinical characteristics. In order to analyze complicated, high-dimensional data and create i...
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