Abstract: Waste classification poses significant challenges owing to rapid societal development and increasing waste generation. Although deep neural networks have demonstrated remarkable results in ...
ABSTRACT: Automatic detection of cognitive distortions from short written text could support large-scale mental-health screening and digital cognitive-behavioural therapy (CBT). Many recent approaches ...
Abstract: Early identification and management of Diabetic Foot Ulcers (DFU) are critical for preventing severe complications, particularly in the Indian subcontinent where DFU prevalence is high. This ...
Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by making a Neural Network in Python from Scratch. We will not use any build in ...
Binary cross-entropy (BCE) is the default loss function for binary classification—but it breaks down badly on imbalanced datasets. The reason is subtle but important: BCE weighs mistakes from both ...
Explore the first part of our series on sleep stage classification using Python, EEG data, and powerful libraries like Sklearn and MNE. Perfect for data scientists and neuroscience enthusiasts!
At the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025) in Suzhou, China this week (November 4-9, 2025), researchers from Bloomberg’s AI Engineering group and its BLAW ...
1 Department of Information Technology and Computer Science, School of Computing and Mathematics, The Cooperative University of Kenya, Nairobi, Kenya. 2 Department of Computing and Informatics, School ...
5.1 RQ1: How does our proposed anomaly detection model perform compared to the baselines? 5.2 RQ2: How much does the sequential and temporal information within log sequences affect anomaly detection?
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