Artificial intelligence is widely applied to automate Diabetic retinopathy diagnosis. Diabetes-related retinal vascular disease one of the world’s most common leading causes blindness and vision impairment. Therefore, automated DR detection systems would greatly benefit early screening treatment prevent loss caused by it. Researchers have proposed several detect abnormalities in images past few years. However, Retinopathy automatic methods traditionally been based on hand-crafted feature...

Diabetic retinopathy (DR) is a primary cause of blindness in which damage occurs to the retina due an accretion sugar levels blood. Therefore, prior detection, classification, and diagnosis DR can prevent vision loss diabetic patients. We proposed novel hybrid approach for detection classification. combined distinctive models make process robust or less error-prone while determining classification based on majority voting method. The work follows preprocessing feature extraction steps. step...

Diabetic retinopathy (DR) is an ocular manifestation of diabetes and the leading cause visual impairment blindness across globe. Early detection treatment DR can salvage from impairment. The manual screening a very laborious time-intensive effort heavily dependent on professional ophthalmologists. In addition, subtle distinction among various retinal biomarkers different grades makes this recognition challenging. To address aforementioned problem, deep neural networks have brought many...

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

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

MiRNAs and lncRNAs are two essential noncoding RNAs. Predicting associations between RNAs diseases can significantly improve the accuracy of early diagnosis.With continuous breakthroughs in artificial intelligence, researchers increasingly use deep learning methods to predict associations. Nevertheless, most existing face major issues: low prediction limitation only being able a single type RNA-disease association. To address these challenges, this paper proposes method called K-Means...

Artificial intelligence plays an essential role in diagnosing lung cancer. Lung cancer is notoriously difficult to diagnose until it has progressed a late stage, making leading cause of cancer-related mortality. fatal if not treated early, this significant issue. Initial diagnosis malignant nodules often made using chest radiography (X-ray) and computed tomography (CT) scans; nevertheless, the possibility benign leads wrong choices. In their first phases, seem very similar. Additionally,...

The rise of vision-threatening diabetic retinopathy (VTDR) underscores the imperative for advanced and efficient early detection mechanisms. With integration Internet Things (IoT) 5G technologies, there is transformative potential VTDR diagnosis, facilitating real-time processing burgeoning volume fundus images (FIs). Combined with artificial intelligence (AI), this offers a robust platform managing vast healthcare datasets achieving unparalleled disease precision. Our study introduces novel...

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

Abstract Data categorization is a top concern in medical data to predict and detect illnesses; thus, it applied modern healthcare informatics. In informatics, machine learning deep models have enjoyed great attention for categorizing improving illness detection. However, the existing techniques, such as features with high dimensionality, computational complexity, long-term execution duration, raise fundamental problems. This study presents novel classification model employing metaheuristic...

Skin cancer is a prevalent health concern, and accurate segmentation of skin lesions crucial for early diagnosis. Existing methods lesion often face trade-offs between efficiency feature extraction capabilities. This paper proposes Dual Segmentation (DuaSkinSeg), deep-learning model, to address this gap by utilizing dual encoders improved performance. DuaSkinSeg leverages pre-trained MobileNetV2 efficient local extraction. Subsequently, Vision Transformer-Convolutional Neural Network...

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

Artificial Intelligence (AI) is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy (VTDR), which a leading cause of visual impairment and blindness worldwide.However, previous automated VTDR detection methods have mainly relied on manual feature extraction classification, to errors.This paper proposes novel classification model that combines different models through majority voting.Our proposed methodology involves preprocessing, data augmentation, extraction,...

Vehicle detection based on very high-resolution (VHR) remote sensing images is beneficial in many fields such as military surveillance, traffic control, and social/economic studies. However, intricate details about the vehicle surrounding background provided by VHR require sophisticated analysis massive data samples, though number of reliable labeled training limited. In practice, augmentation often leveraged to solve this conflict. The traditional strategy uses a combination rotation,...

Finger vein biometric technology has gained a lot of popularity over recent years. This is primarily due to the increased security and reliability level that comes with its non-intrusive nature. Non-intrusiveness became inevitable pandemic COVID-19. paper introduces unique lightweight image enhancement method for person identification using Convolutional Neural Networks (CNN). As pre-processing steps, Contrast Limited Adaptive Histogram Equalization (CLAHE) followed by gamma correction...

Farming is cultivating the soil, producing crops, and keeping livestock. The agricultural sector plays a crucial role in country's economic growth. This research proposes two-stage machine learning framework for agriculture to improve efficiency increase crop yield. In first stage, algorithms generate data extensive far-flung areas forecast crops. recommended crops are based on various factors such as weather conditions, soil analysis, amount of fertilizers pesticides required. second...

Glaucoma is a progressive eye disease that can lead to blindness if left untreated. Early detection crucial prevent vision loss, but current manual scanning methods are expensive, time-consuming, and require specialized expertise. This study presents novel approach using the Enhanced Grey Wolf Optimized Support Vector Machine (EGWO-SVM) method. The proposed method involves preprocessing steps such as removing image noise adaptive median filter (AMF) feature extraction previously processed...

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