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OralNet: deep learning fusion for oral cancer identification from lips and tongue images using stochastic gradient based logistic regression
Timely detection of oral cancer plays a critical role in improving survival rates. While traditional biopsy procedures can be invasive and...
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Fourier Analysis Meets Runtime Analysis: Precise Runtimes on Plateaus
We propose a new method based on discrete Fourier analysis to analyze the time evolutionary algorithms spend on plateaus. This immediately gives a...
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Nearly Time-Optimal Kernelization Algorithms for the Line-Cover Problem with Big Data
Based on well-known complexity theory conjectures, any polynomial-time kernelization algorithm for the NP-hard Line- Cover problem produces a kernel...
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How natural language processing derived techniques are used on biological data: a systematic review
The decoding of the human genome, completed two decades ago, marked a revolutionary moment in biology by introducing a vast amount of data. This...
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EEG source imaging of hand movement-related areas: an evaluation of the reconstruction and classification accuracy with optimized channels
The hand motor activity can be identified and converted into commands for controlling machines through a brain-computer interface (BCI) system....
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Decoupling Anomaly Discrimination and Representation Learning: Self-supervised Learning for Anomaly Detection on Attributed Graph
Anomaly detection on attributed graphs is a crucial topic for practical applications. Existing methods suffer from semantic mixture and imbalance...
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Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing
PurposeUnderstanding early vascular ageing has become crucial for preventing adverse cardiovascular events. To this respect, recent AI-based risk...
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Technologies for design-build-test-learn automation and computational modelling across the synthetic biology workflow: a review
Motivated by the need to parameterize and functionalize dynamic, multiscale simulations, as well as bridge the gap between advancing in silico and...
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Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset
Gastrointestinal (GI) disorders, encompassing conditions like cancer and Crohn’s disease, pose a significant threat to public health. Endoscopic...
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A hybrid approach based on multipath Swin transformer and ConvMixer for white blood cells classification
White blood cells (WBC) play an effective role in the body’s defense against parasites, viruses, and bacteria in the human body. Also, WBCs are...
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Trainable Gaussian-based activation functions for sensor-based human activity recognition
Neural networks’ capability to model non-linear relationships strongly depends on their activation functions (AFs). This dependency makes the search...
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Runtime Analysis of Competitive Co-evolutionary Algorithms for Maximin Optimisation of a Bilinear Function
Co-evolutionary algorithms have a wide range of applications, such as in hardware design, evolution of strategies for board games, and patching...
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Computational model of engagement with stigmatised sentiment: COVID and general vaccine discourse on social media
The growth rate of new social media users continues to surpass new Internet users and new unique mobile phone subscribers and this trend remains...
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A machine learning-based early diagnosis model for chronic kidney disease using SPegasos
Chronic Kidney Disease is now one of the most severe illnesses that requires an immediate diagnosis. Previous research has shown that...
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Automated phase-type distribution fitting via expectation maximization
In numerous practical domains such as reliability and performance engineering, finance, healthcare, and supply chain management, a common challenge...
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Exploiting biochemical data to improve osteosarcoma diagnosis with deep learning
Early and accurate diagnosis of osteosarcomas (OS) is of great clinical significance, and machine learning (ML) based methods are increasingly...
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Few-shot classification with prototypical neural network for hospital flow recognition under uncertainty
Accurately identifying and analyzing patient and personnel flow patterns within healthcare facilities is crucial for optimizing operational...
