A Feature Fusion Attention-based Deep Learning Algorithm for Mammographic Architectural Distortion Classification.

Architectural Distortion (AD) is a common abnormality in digital mammograms, alongside masses and microcalcifications. Detecting AD in dense breast tissue is particularly challenging due to its heterogeneous asymmetries and subtle presentation. Factors such as location, size, shape, texture, and variability in patterns contribute to reduced sensitivity.

An integrated CSPPC and BiLSTM framework for malicious URL detection.

With the rapid development of the internet, phishing attacks have become more diverse, making phishing website detection a key focus in cybersecurity. While machine learning and deep learning have led to various phishing URL detection methods, many remain incomplete, limiting accuracy. This paper proposes CSPPC-BiLSTM, a malicious URL detection model based on BiLSTM (Bidirectional Long Short-Term Memory, BiLSTM).

Precision and efficiency in skin cancer segmentation through a dual encoder deep learning model.

Skin cancer is a prevalent health concern, and accurate segmentation of skin lesions is crucial for early diagnosis. Existing methods for skin lesion segmentation often face trade-offs between efficiency and feature extraction capabilities. This paper proposes Dual Skin Segmentation (DuaSkinSeg), a deep-learning model, to address this gap by utilizing dual encoders for improved performance.

A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM.

Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.

Predicting noncoding RNA and disease associations using multigraph contrastive learning.

MiRNAs and lncRNAs are two essential noncoding RNAs. Predicting associations between noncoding RNAs and diseases can significantly improve the accuracy of early diagnosis.With the continuous breakthroughs in artificial intelligence, researchers increasingly use deep learning methods to predict associations.

m5c-iEnsem: 5-methylcytosine sites identification through ensemble models.

Motivation: 5-Methylcytosine (m5c), a modified cytosine base, arises from adding a methyl group at the 5th carbon position. This modification is a prevalent form of post-transcriptional modification (PTM) found in various types of RNA. Traditional laboratory techniques often fail to provide rapid and accurate identification of m5c sites.

iDNA-ITLM: An interpretable and transferable learning model for identifying DNA methylation.

In this study, from the perspective of image processing, we propose the iDNA-ITLM model, using a novel data enhance strategy by continuously self-replicating a short DNA sequence into a longer DNA sequence and then embedding it into a high-dimensional matrix to enlarge the receptive field, for identifying DNA methylation sites. Our model consistently outperforms the current state-of-the-art sequence-based DNA methylation site recognition methods when evaluated on 17 benchmark datasets that cover multiple species and include three DNA methylation modifications (4mC, 5hmC, and 6mA). The experimental results demonstrate the robustness and superior performance of our model across these datasets.

Prediction of miRNAs and diseases association based on sparse autoencoder and MLP.

MicroRNAs (miRNAs) are small and non-coding RNA molecules which have multiple important regulatory roles within cells. With the deepening research on miRNAs, more and more researches show that the abnormal expression of miRNAs is closely related to various diseases. The relationship between miRNAs and diseases is crucial for discovering the pathogenesis of diseases and exploring new treatment methods.

A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification.

In response to the growing number of diabetes cases worldwide, Our study addresses the escalating issue of diabetic eye disease (DED), a significant contributor to vision loss globally, through a pioneering approach. We propose a novel integration of a Genetic Grey Wolf Optimization (G-GWO) algorithm with a Fully Convolutional Encoder-Decoder Network (FCEDN), further enhanced by a Kernel Extreme Learning Machine (KELM) for refined image segmentation and disease classification. This innovative combination leverages the genetic algorithm and grey wolf optimization to boost the FCEDN's efficiency, enabling precise detection of DED stages and differentiation among disease types.

Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization.

A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel.

BC-QNet: A quantum-infused ELM model for breast cancer diagnosis.

The timely and accurate diagnosis of breast cancer is pivotal for effective treatment, but current automated mammography classification methods have their constraints. In this study, we introduce an innovative hybrid model that marries the power of the Extreme Learning Machine (ELM) with FuNet transfer learning, harnessing the potential of the MIAS dataset. This novel approach leverages an Enhanced Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO) within the ELM framework, elevating its performance.

iDNA-OpenPrompt: OpenPrompt learning model for identifying DNA methylation.

DNA methylation is a critical epigenetic modification involving the addition of a methyl group to the DNA molecule, playing a key role in regulating gene expression without changing the DNA sequence. The main difficulty in identifying DNA methylation sites lies in the subtle and complex nature of methylation patterns, which may vary across different tissues, developmental stages, and environmental conditions. Traditional methods for methylation site identification, such as bisulfite sequencing, are typically labor-intensive, costly, and require large amounts of DNA, hindering high-throughput analysis.

Advancing single-cell RNA-seq data analysis through the fusion of multi-layer perceptron and graph neural network.

The advancement of single-cell sequencing technology has smoothed the ability to do biological studies at the cellular level. Nevertheless, single-cell RNA sequencing (scRNA-seq) data presents several obstacles due to the considerable heterogeneity, sparsity and complexity. Although many machine-learning models have been devised to tackle these difficulties, there is still a need to enhance their efficiency and accuracy.

Improved Support Vector Machine based on CNN-SVD for vision-threatening diabetic retinopathy detection and classification.

The integration of artificial intelligence (AI) in diagnosing diabetic retinopathy, a major contributor to global vision impairment, is becoming increasingly pronounced. Notably, the detection of vision-threatening diabetic retinopathy (VTDR) has been significantly fortified through automated techniques. Traditionally, the reliance on manual analysis of retinal images, albeit slow and error-prone, constituted the conventional approach.

The facile synthesis of and light-driven water splitting on a hetero-metallic bismuth oxide catalyst.

The process of water photo-electrolysis possesses the capability to generate sustainable and renewable hydrogen fuels, consequently addressing the challenge of the irregularity of solar energy. Thus, developing highly-efficient and low-cost electrocatalysts for the use in contemporary renewable energy devices is critical. Herein, we report the fabrication of a novel BaCeFeBiO nanocrystalline material through a one-step solvothermal route using a post-annealing process at 500 °C.

IGWO-IVNet3: DL-Based Automatic Diagnosis of Lung Nodules Using an Improved Gray Wolf Optimization and InceptionNet-V3.

Artificial intelligence plays an essential role in diagnosing lung cancer. Lung cancer is notoriously difficult to diagnose until it has progressed to a late stage, making it a leading cause of cancer-related mortality. Lung cancer is fatal if not treated early, making this a significant issue.

Understanding the usability issues in contact management of illiterate and semi-literate users.

The effective utilization of a communication channel like calling a person involves two steps. The first step is storing the contact information of another user, and the second step is finding contact information to initiate a voice or text communication. However, the current smartphone interfaces for contact management are mainly textual; which leaves many emergent users at a severe disadvantage in using this most basic functionality to the fullest.