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Intelligent Personality Assessment and Verification from Handwriting using Machine Learning
It is possible to tell a lot about a person just by looking at their handwriting. The way someone writes might tell you a lot about who, they are as...
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Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling
Immunoglobulins (Ig), which exist either as B-cell receptors (BCR) on the surface of B cells or as antibodies when secreted, play a key role in the...
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k-filters and k-\(\{^*\}\)-congruences of core regular double Stone algebras
In this paper, we investigate various elegant filters and congruences of the class of core regular double Stone algebras (briefly CRD -Stone...
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Learning to sculpt neural cityscapes
We introduce a system that learns to sculpt 3D models of massive urban environments. The majority of humans live their lives in urban environments,...
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Integrating metaheuristics and artificial intelligence for healthcare: basics, challenging and future directions
Accurate and rapid disease detection is necessary to manage health problems early. Rapid increases in data amount and dimensionality caused...
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METASEED: a novel approach to full-length 16S rRNA gene reconstruction from short read data
BackgroundWith the emergence of Oxford Nanopore technology, now the on-site sequencing of 16S rRNA from environments is available. Due to the error...
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Picture fuzzy filters on residuated lattices
Filters play an important role in studying fuzzy logics. From a logical point of view, filters correspond to sets of provable formulae. In this...
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Optimization of the different controller parameters via OBL approaches based artificial ecosystem optimization involving fitness distance balance guiding mechanism for efficient motor speed regulation of DC motor
This study proposes a new optimization approach, which is called as artificial ecosystem optimization algorithm with fitness-distance balance guiding...
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Improving laryngeal cancer detection using chaotic metaheuristics integration with squeeze-and-excitation resnet model
Laryngeal cancer (LC) represents a substantial world health problem, with diminished survival rates attributed to late-stage diagnoses. Correct...
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Convective instability analysis of the flow due to a linearly shrinking sheet with uniform suction
We explore the linear stability of a two-dimensional flow induced due to streamwise linear shrinking of a flat surface. Dual solutions for mean flow...
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L2XGNN: learning to explain graph neural networks
Graph Neural Networks (GNNs) are a popular class of machine learning models. Inspired by the learning to explain (L2X) paradigm, we propose L2xGnn , a...
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Hierarchical contrastive representation for zero shot learning
Zero-shot learning aims to identify unseen (novel) objects, using only labeled samples from seen (base) classes. Existing methods usually learn...
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Achieving accurate and balanced regional electric vehicle charging load forecasting with a dynamic road network: a case study of Lanzhou City
AbstractSpatial and temporal predictions of electric vehicle (EV) charging loads provide a basis for further research on synergistic operation of...
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expHRD: an individualized, transcriptome-based prediction model for homologous recombination deficiency assessment in cancer
BackgroundHomologous recombination deficiency (HRD) stands as a clinical indicator for discerning responsive outcomes to platinum-based chemotherapy...
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Deep learning framework for stock price prediction using long short-term memory
Forecasting stock prices is always considered as complicated process due to the dynamic and noisy characteristics of stock data influenced by...
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Prediction of servo industry development in China by an optimized reverse Hausdorff fractional discrete grey power model
In order to accurately predict the development of the servo industry in China, this study proposes a Hausdorff fractional reverse accumulated grey...
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Exploring Brazilian Teachers’ Perceptions and a priori Needs to Design Smart Classrooms
Smart classrooms offer innovative opportunities to enhance teaching and learning. However, most existing research in this field predominantly focuses...
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Building RadiologyNET: an unsupervised approach to annotating a large-scale multimodal medical database
BackgroundThe use of machine learning in medical diagnosis and treatment has grown significantly in recent years with the development of...
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Anomaly analytics in data-driven machine learning applications
Machine learning is used widely to create a range of prediction or classification models. The quality of the machine learning (ML) models depends not...
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DGNN-MN: Dynamic Graph Neural Network via memory regenerate and neighbor propagation
Dynamic Graph Neural Network (DGNN) models have been widely used for modelling, prediction and recommendation tasks in domains such as e-commerce and...
