The rapid population growth results in a crucial problem in the early detection of diseases inmedical *** all the cancers unveiled,breast cancer is considered the second most severe ***,an exponential rising in death ...
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The rapid population growth results in a crucial problem in the early detection of diseases inmedical *** all the cancers unveiled,breast cancer is considered the second most severe ***,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical *** recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death *** this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated *** proposed algorithm is evaluated using four publicly available breast cancer *** evaluation results show the effectiveness of the proposed approach from the accuracy and speed *** prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted *** addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed *** best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.
The proposed work objective is to adopt the non-dominated sorting genetic algorithm II (NSGA-II), a type of MOEA (multi-objective evolutionary algorithms), to reduce the dimensionality and identify the most relevant f...
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The goal of this research is to create a sophisticated system for gathering student data that is suitable for use in classroom settings. The approach begins with taking pictures of every student in the class in a meth...
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This research work proposes an automated system for identifying Capuchin bird calls from audio recordings in a forest environment. The system uses convolutional neural networks (CNNs) to classify short segments of aud...
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ISBN:
(纸本)9798331519056
This research work proposes an automated system for identifying Capuchin bird calls from audio recordings in a forest environment. The system uses convolutional neural networks (CNNs) to classify short segments of audio data represented as spectrograms. The automatic identification and spatial analysis of capuchin bird calls in a forest ecosystem. We are using the power of deep learning to develop a system to efficiently process large audio recordings, which will provide researchers and conservationists with valuable information about the distribution of capuchin birds. We achieve this through a multi-step process. First, raw audio data recorded in a forest environment is converted into signals to visually represent sound pressure fluctuations. These waveforms are later converted into spectrograms, providing a visual representation of the frequencies present in the audio data over time. This spectrogram serves as an ideal input signal for CNN due to its similarity to images. This system lies in its ability to analyse audio data from various locations in the forest. By comparing the number of capuchin calls identified in records from different regions, we can create spatial distribution maps that highlight areas with the highest densities of capuchins. This information has proven invaluable to researchers studying capuchin behaviour, habitat preferences, and population dynamics. It could also be an important tool in conservation efforts to target stocks and protect areas with high capuchin densities. This approach offers significant advantages over existing methods that rely on human experts manually listening and identifying bird sounds. Automating processes not only improves efficiency, but also reduces the potential for human error and bias. Additionally, the system's ability to analyse large data sets will allow researchers to gain larger-scale insights into capuchin populations, leading to a more complete understanding of their distribution and conservation need
The evolution that has been observed in the dynamic field of object identification in the past few years is remarkable, because of the integration of sophisticated learning techniques. The review presented in this pap...
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This paper explores the complex field of student stress analysis. Recognizing that stressors in educational contexts are complicated, this work makes use of cutting-edge Machine and Deep learning approaches to negotia...
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Plant diseases require quick and accurate identification methods since they significantly reduce agricultural production's output and quality. Traditional methods of plant protection, which depend on visual assess...
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The forthcoming forensic sciences standard ISO/IEC 21043 is a methodological and technical standard, currently at the stage of Draft International Standard. When adopted, it will apply to all forensic disciplines, inc...
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This paper presents an in-depth exploratory quantitative study of the interaction between multimedia and textual components in online manipulative content. We discuss relations between content layers (such as proof or...
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When renewable energy sources (RESs) are used to generate energy at user premises, the application of local energy creation and peer-to-peer (P2P) energy exchange in the local market can help reduce energy consumption...
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