Honeybees play a crucial role in the agriculture industry because they pollinate approximately 75% of all flowering crops. However, every year, the number of honeybees continues to decrease. Consequently, numerous res...
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Honeybees play a crucial role in the agriculture industry because they pollinate approximately 75% of all flowering crops. However, every year, the number of honeybees continues to decrease. Consequently, numerous researchers in various fields have persistently attempted to solve this problem. Acoustic scene classification, using sounds recorded from beehives, is an approach that can be applied to detect changes inside beehives. This method can be used to determine intervals that threaten a beehive. Currently, studies on sound analysis, using deep learning algorithms integrated with various data preprocessing methods that extract features from sound signals, continue to be conducted. However, there is little insight into how deep learning algorithms recognize audio scenes, as demonstrated by studies on image recognition. Therefore, in this study, we used a mel spectrogram, mel-frequency cepstral coefficients (MFCCs), and a constant-Q transform to compare the performance of conventional machine learning models to that of convolutional neural network (CNN) models. We used the support vector machine, random forest, extreme gradient boosting, shallow CNN, and VGG-13 models. Using gradient-weighted class activation mapping (Grad-CAM), we conducted an analysis to determine how the best-performing CNN model recognized audio scenes. The results showed that the VGG-13 model, using MFCCs as input data, demonstrated the best accuracy (91.93%). Additionally, based on the precision, recall, and F1-score for each class, we established that sounds other than those from bees were effectively recognized. Further, we conducted an analysis to determine the MFCCs that are important for classification through the visualizations obtained by applying Grad-CAM to the VGG-13 model. We believe that our findings can be used to develop a monitoring system that can consistently detect abnormal conditions in beehives early by classifying the sounds inside beehives.
We study the convergence of a class of gradient-based Model-Agnostic Meta-learning (MAML) methods and characterize their overall complexity as well as their best achievable accuracy in terms of gradient norm for nonco...
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We study the convergence of a class of gradient-based Model-Agnostic Meta-learning (MAML) methods and characterize their overall complexity as well as their best achievable accuracy in terms of gradient norm for nonconvex loss functions. We start with the MAML method and its first-order approximation (FO-MAML) and high-light the challenges that emerge in their analysis. By overcoming these challenges not only we provide the first theoretical guarantees for MAML and FO-MAML in nonconvex settings, but also we answer some of the unanswered questions for the implementation of these algorithms including how to choose their learning rate and the batch size for both tasks and datasets corresponding to tasks. In particular, we show that MAML can find an epsilon-first-order stationary point (epsilon-FOSP) for any positive epsilon after at most O(1/epsilon(2)) iterations at the expense of requiring second-order information. We also show that FO-MAML which ignores the second-order information required in the update of MAML cannot achieve any small desired level of accuracy, i.e., FO-MAML cannot find an epsilon-FOSP for any epsilon > 0. We further propose a new-variant of the MAML algorithm called Hessian-free MAML which preserves all theoretical guarantees of MAML, without requiring access to second-order information.
Successful performance of machine learning approaches for object classification requires training with data sets that are good representations of actual field data. Most open source image databases, while large in siz...
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ISBN:
(纸本)9781510636040
Successful performance of machine learning approaches for object classification requires training with data sets that are good representations of actual field data. Most open source image databases, while large in size, are not representative of the type of scenes encountered by Army ground missions. The CCDC Army Research Laboratory hosts datasets, some collected recently, and some a few years ago that focus on Army scenarios and are thus an appropriate source of training data for defense applications. This paper presents examples of several of these datasets along with conditions of their availability to external research collaborators.
The overall evaluation of the research work is based on the liver disease classification and prediction performed by comparing different working procedures and merits of each method with other works in terms of perfor...
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Inverter circuits are widely used in power electronics applications such as electric motor control, induction heating or different Alternating Current (AC) loads. The control signal applied to the switching elements c...
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Solar hot water (SHW) systems are viable and sustainable devices for hot water domestic and industrial energy needs. Nevertheless, the efficient operation of these systems can be compromised if the necessary maintenan...
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In recent times many researchers are motivated by the success of machine learning algorithms [15] in the field of computer vision to improve the performance of plant disease detection. Agriculture in India has many cr...
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Analysing coronary artery plaque segments with respect to their functional significance and therefore their influence to patient management in a non-invasive setup is an important subject of current research. In this ...
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A novel way to apply machine learning algorithms on the incremental capacity analysis (dQ/dV) is developed to identify battery cycling conditions under different temperatures and working SOC ranges. Batteries are cycl...
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The established analytical calculation method for the steady state thermal rating of power cables is given by the IEC 60287. Anyhow, due to a general increase in complexity in energy systems and the speed of the trans...
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