This study develops an advanced automated prediction system using Machine Learning (ML) techniques to identify diabetes early. The research employs the WBSMOTE method for data preprocessing, addresses class imbalances...
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In this paper, authors propose a spatial difference Toeplitz algorithm for effective direction of arrival (DOA) estimation of mixed signals that contain both uncorrelated and coherent components. Unlike existing metho...
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This paper introduces 'Smart Irrigation Yield Optimization (SIYO),' a sophisticated IoT-based smart irrigation system aimed at significantly improving crop yield and growth predictions. Traditional irrigation ...
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In the digital age, online news websites are indispensable communication channels for disseminating vital information within academic communities. This paper proposes a comprehensive approach to enhancing user experie...
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Resume Parser and Job Description Matcher is an innovative project aimed at enhancing the efficiency and accuracy of job candidate screening and matching processes. Leveraging natural language processing (NLP) techniq...
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This paper introduces OBL-AHO, an enhanced Archerfish Hunting Optimizer (AHO) variant that integrates Opposition-Based Learning (OBL) to improve AHO's exploratory capabilities. By leveraging opposite solutions alo...
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A new hybrid approach for diagnosing schizophrenia combines graph convolutional networks (GCNs) with long short-term memory (LSTM) networks, leveraging GCNs for spatial connectivity and LSTMs for temporal modeling. Th...
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In this study, the predictive modeling employed is the support vector machine (SVM) method, which efficiently handles highly complex health data. Despite the prevalence of stressful lifestyles today, it seems unlikely...
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To improve resource mapping in coal exploration, this research introduces a new method that combines Internet of Things (IoT) sensor-based drilling devices with Support Vector Machine (SVM) algorithms. Coal exploratio...
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Canonical correlation analysis (CCA) and its improved algorithms play an important role for SSVEP classification, mainly focusing on improving the signal-to-noise ratio by reducing the electroencephalogram (EEG) backg...
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