The economy of scale provided by cloud attracts a growing number organizations and industrial companies to deploy their applications in data centers (CDCs) provide services users around the world. uncertainty arriving tasks makes it big challenge for private CDC cost-effectively schedule delay bounded without exceeding bounds. Unlike previous studies, this paper takes into account cost minimization problem hybrid clouds, where energy price execution public clouds both show temporal...

Abstract This paper explores the concept of smart cities and role Internet Things (IoT) machine learning (ML) in realizing a data-centric environment. Smart leverage technology data to improve quality life for citizens enhance efficiency urban services. IoT have emerged as key technologies enabling city solutions that rely on large-scale collection, analysis, decision-making. presents an overview cities’ various applications discusses challenges associated with implementing environments. The...

Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection video Speech Recognition. CNN is a special type of Neural Network, which compelling effective learning ability to learn features at several steps during augmentation the data. Recently, interesting inspiring...

Architectural Distortion (AD) is a common abnormality in digital mammograms, alongside masses and microcalcifications. Detecting AD dense breast tissue particularly challenging due to its heterogeneous asymmetries subtle presentation. Factors such as location, size, shape, texture, variability patterns contribute reduced sensitivity. To address these challenges, we propose novel feature fusion-based Vision Transformer (ViT) attention network, combined with VGG-16, improve accuracy efficiency...

A key factor of win–win cloud economy is how to trade off between the application performance from customers and profit providers. Current researches on resource allocation do not sufficiently address issues minimizing energy cost maximizing revenue for various applications running in virtualized data centers (VCDCs). This paper presents a new approach optimize VCDC based service-level agreements (SLAs) service providers customers. precise model external internal request arrival rates...

Cataract is one of the most serious eye diseases leading to blindness. Early detection and treatment can reduce rate blindness in cataract patients. However, professional knowledge ophthalmologists necessary for clinical detection. Therefore, potential costs may make it difficult widespread use prevent Artificial intelligence assisted diagnosis based on medical images has attracted more attention researchers. Many studies have focused pre-defined feature sets classification, but predefined...

Patients with breast cancer are prone to serious health-related complications higher mortality. The primary reason might be a misinterpretation of radiologists in recognizing suspicious lesions due technical issues imaging qualities and heterogeneous densities which increases the false-(positive negative) ratio. Early intervention is significant establishing an up-to-date prognosis process can successfully mitigate disease recovery. manual screening abnormalities through traditional machine...

Cataract is one of the most prevalent causes blindness in industrialized world, accounting for more than 50% blindness. Early detection and treatment can reduce suffering cataract patients prevent visual impairment from turning into But expertise trained eye specialists necessary clinical grading, which may cause difficulties to everybody's early intervention due underlying costs. Existing studies on automatic grading based fundus images utilize a predefined set image features that provide...

The blood vessels are the primary anatomical structure that can be visible in retinal images. segmentation of has been accepted worldwide for diagnosis both cardiovascular (CVD) and diseases. Thus, it requires an appropriate vessel method automatic detection diseases such as diabetic retinopathy cataract. using computer-aided (CAD) help people to avoid risks visual impairment save medical resources. This survey presents a comparative analysis various machine learning deep learning-based...

Increasingly, more people are suffering from the effects of air pollution. This study took Beijing as an example and proposed attention-based quality predictor (AAQP) that could better protect The AAQP is a seq2seq model, it exploits historical data weather to predict future indexes. Although existing research has promoted for prediction, there still two problems. First, slow training speed so original RNN in encoder was replaced with fully connected accelerate process. Position embedding...

Recently, deep neural network (DNN) models work incredibly well, and edge computing has achieved great success in real-world scenarios, such as fault diagnosis for large-scale rotational machinery. However, DNN training takes a long time due to its complex calculation, which makes it difficult optimize retrain models. To address an issue, this proposes novel model by combining binarized DNNs (BDNNs) with improved random forests (RFs). First, BDNN-based feature extraction method binary...

Breast cancer is one of the worst illnesses, with a higher fatality rate among women globally. detection needs accurate mammography interpretation and analysis, which challenging for radiologists owing to intricate anatomy breast low image quality. Advances in deep learning-based models have significantly improved lesions' detection, localization, risk assessment, categorization. This study proposes novel convolutional neural network (ConvNet) that reduces human error diagnosing malignancy...

Diagnosing breast cancer masses and calcification clusters have paramount significance in mammography, which aids mitigating the disease's complexities curing it at early stages. However, a wrong mammogram interpretation may lead to an unnecessary biopsy of false-positive findings, reduces patient's survival chances. Consequently, approaches that learn discern can reduce number misconceptions incorrect diagnoses. Conventionally used classification models focus on feature extraction...

The multi-objective optimization problem is difficult to solve with conventional methods and algorithms because there are conflicts among several objectives functions. Through the efforts of researchers experts from different fields for last 30 years, research application evolutionary (MOEA) have made excellent progress in solving such problems. MOEA has become one primary used technologies realm optimization. It also a hotspot computation community. This survey provides comprehensive...

A method is proposed for recognizing and predicting non-linear systems employing a radial basis function neural network (RBFNN) robust hybrid particle swarm optimization (HPSO) approach. PSO coupled with spiral-shaped mechanism (HPSO-SSM) to optimize the performance by mitigating its constraints, such as sluggish convergence local minimum dilemma. Three advancements are incorporated into hypothesized HPSO-SSM algorithms achieve remarkable results. First, diversity of search process promoted...