Academic Articles & Research Papers

Global patterns of gully occurrence and their sensitivity to environmental changes

Gully formation is a significant driver of soil erosion and land degradation worldwide and often leads to important downstream impacts. Nonetheless, our understanding of the global patterns and the factors controlling this process remains l...

LearningEMS: A Unified Framework and Open-Source Benchmark for Learning-Based Energy Management of Electric Vehicles

An effective energy management strategy (EMS) is essential to optimize the energy efficiency of electric vehicles (EVs). With the advent of advanced machine learning techniques, the focus on developing sophisticated EMS for EVs is increasin...

The voice of depression: speech features as biomarkers for major depressive disorder

Abstract Background Psychiatry faces a challenge due to the lack of objective biomarkers, as current assessments are based on subjective evaluations. Automated speech analysis shows promise in detecting symptom severity in depressed patient...

Integrating task-state electroencephalography and motor function for intelligent assessment of recovery status from prolonged disorders of consciousness

Abstract Background Accurately assessing recovery in patients with prolonged disorders of consciousness (PDoC) is essential for evaluating the therapeutic efficacy and optimizing clinical care. However, substantial individual variability an...

Machine learning-based diagnostic prediction of minimal change disease: model development study

Abstract Minimal change disease (MCD) is a common cause of nephrotic syndrome. Due to its rapid progression, early detection is essential; however, definitive diagnosis requires invasive kidney biopsy. This study aims to develop non-invasiv...

A feature selection and scoring scheme for dimensionality reduction in a machine learning task

Selection of important features is very vital in machine learning tasks involving high-dimensional dataset with large features. It helps in reducing the dimensionality of a dataset and improving model performance. Most of the feature select...

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems ...

Possible Worlds, Artificial Intelligence, and Narrative Theory

From the Publisher: In this important contribution to narrative theory, Marie-Laure Ryan applies insights from artificial intelligence and the theory of possible worlds to the study of narrative and fiction. For Ryan, the theory of possibl...

A deep learning model for automated identification of age-related macular degeneration atrophy.

On pethidine: The 'accidental' opioid that became a 'model' analgesic agent.

Artificial intelligence model to predict pregnancy and multiple pregnancy risk following in vitro fertilization-embryo transfer (IVF-ET)

Objective: To decrease multiple pregnancy risk and sustain optimal pregnancy chance by choosing suitable number of embryos during transfer, this study aims to construct artificial intelligence models to predict the pregnancy outcome and mul...

Selection of target-binding proteins from the information of weakly enriched phage display libraries by deep sequencing and machine learning

Despite the advances in surface-display systems for directed evolution, variants with high affinity are not always enriched due to undesirable biases that increase target-unrelated variants during biopanning. Here, our goal was to design a ...

Predicting Choroidal Nevus Transformation to Melanoma Using Machine Learning

Purpose: To develop and validate machine learning (ML) models to predict choroidal nevus transformation to melanoma based on multimodal imaging at initial presentation. Design: Retrospective multicenter study. Participants: Patients diagnos...

Evaluating designers’ acceptance of AI generated content using TAM and TRI frameworks

Abstract Artificial intelligence technology, based on big data algorithms, can replace much of the repetitive and rule-based work. However, whether AI can substitute the roles of artists or designers in the art and design industry, which pr...

Leveraging AI to Estimate Caribou Lichen in UAV Orthomosaics from Ground Photo Datasets

Relating ground photographs to UAV orthomosaics is a key linkage required for accurate multi-scaled lichen mapping. Conventional methods of multi-scaled lichen mapping, such as random forest models and convolutional neural networks, heavily...

Time Series Impact Through Topic Modeling

A time-series of numerical data and a sequence of time-ordered documents are often correlated. This paper aims at modeling the impact that the underlying themes discussed in the text data have on the time series. To do so, we introduce an o...

Representations and Benchmarking of Modern Visual SLAM Systems

Simultaneous Localisation And Mapping (SLAM) has long been recognised as a core problem to be solved within countless emerging mobile applications that require intelligent interaction or navigation in an environment. Classical solutions to ...

The role of artificial intelligence in disinformation

Abstract Artificial intelligence (AI) systems are playing an overarching role in the disinformation phenomenon our world is currently facing. Such systems boost the problem not only by increasing opportunities to create realistic AI-generat...

Artificial intelligence in orthopaedic surgery

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need ...

The Role of Artificial Intelligence in Early Cancer Diagnosis

Improving the proportion of patients diagnosed with early-stage cancer is a key priority of the World Health Organisation. In many tumour groups, screening programmes have led to improvements in survival, but patient selection and risk stra...