FocalNeXt: A ConvNeXt augmented FocalNet architecture for lung cancer classification from CT-scan images

Expert Systems with Applications, Год журнала: 2024, Номер unknown, С. 125553 - 125553

Опубликована: Окт. 1, 2024

Язык: Английский

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Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive Maintenance Framework

Sensors, Год журнала: 2024, Номер 24(24), С. 7918 - 7918

Опубликована: Дек. 11, 2024

Sensor networks generate vast amounts of data in real-time, which challenges existing predictive maintenance frameworks due to high latency, energy consumption, and bandwidth requirements. This research addresses these limitations by proposing an edge-cloud hybrid framework, leveraging edge devices for immediate anomaly detection cloud servers in-depth failure prediction. A K-Nearest Neighbors (KNNs) model is deployed on detect anomalies reducing the need continuous transfer cloud. Meanwhile, a Long Short-Term Memory (LSTM) analyzes time-series analysis, enhancing scheduling operational efficiency. The framework’s dynamic workload management algorithm optimizes task distribution between resources, balancing usage, consumption. Experimental results show that approach achieves 35% reduction 28% decrease 60% usage compared cloud-only solutions. framework offers scalable, efficient solution real-time maintenance, making it highly applicable resource-constrained, data-intensive environments.

Язык: Английский

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BankNet: Real-Time Big Data Analytics for Secure Internet Banking

Big Data and Cognitive Computing, Год журнала: 2025, Номер 9(2), С. 24 - 24

Опубликована: Янв. 26, 2025

The rapid growth of Internet banking has necessitated advanced systems for secure, real-time decision making. This paper introduces BankNet, a predictive analytics framework integrating big data tools and BiLSTM neural network to deliver high-accuracy transaction analysis. BankNet achieves exceptional performance, with Root Mean Squared Error 0.0159 fraud detection accuracy 98.5%, while efficiently handling rates up 1000 Mbps minimal latency. By addressing critical challenges in operational efficiency, establishes itself as robust support system modern banking. Its scalability precision make it transformative tool enhancing security trust financial services.

Язык: Английский

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Deep Learning‐Assisted Computer‐Aided Diagnosis System for Early Detection of Lung Cancer

Journal of Clinical Ultrasound, Год журнала: 2025, Номер unknown

Опубликована: Янв. 29, 2025

ABSTRACT Purpose The largest cause of cancer‐related fatalities worldwide is lung cancer. dimensions and positioning the primary tumor, presence lesions, type cancer like malignant or benign, good mental health diagnosis all play a part in disease. Methods Three processes should be used by standard computer‐assisted (CAD) systems to detect cancer: preprocessing, feature extraction, classification. Fast nonlocal means filter first for preprocessing (FNLM). pictures are processed using Binary Grasshopper Optimization Algorithm (BGOA) extract features. Results 10 levels neural network architecture which automatically gathers data categorizes them added current study's suggested model, subtracts five Imagenet. Using same Modèle dataset, proposed model was compared deep learning techniques. Conclusion In terms accuracy sensitivity, performed better than existing techniques (99.53% 98.95% sensitivity). effectiveness strategy superior that alternative methods when it near true positive values at ROC curve.

Язык: Английский

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An Efficient Lightweight Multi Head Attention Gannet Convolutional Neural Network Based Mammograms Classification

International Journal of Medical Robotics and Computer Assisted Surgery, Год журнала: 2025, Номер 21(1)

Опубликована: Фев. 1, 2025

This research aims to use deep learning create automated systems for better breast cancer detection and categorisation in mammogram images, helping medical professionals overcome challenges such as time consumption, feature extraction issues limited training models. introduced a Lightweight Multihead attention Gannet Convolutional Neural Network (LMGCNN) classify images effectively. It used wiener filtering, unsharp masking, adaptive histogram equalisation enhance remove noise, followed by Grey-Level Co-occurrence Matrix (GLCM) extraction. Ideal selection is done self-adaptive quantum equilibrium optimiser with artificial bee colony. The assessed on two datasets, CBIS-DDSM MIAS, achieving impressive accuracy rates of 98.2% 99.9%, respectively, which highlight the superior performance LMGCNN model while accurately detecting compared previous method illustrates potential aiding initial accurate detection, possibly leading improved patient outcomes.

Язык: Английский

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A two stage blood cell detection and classification algorithm based on improved YOLOv7 and EfficientNetv2

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 11, 2025

Язык: Английский

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The Intersection of AI, Cloud Computing, and Healthcare

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 379 - 404

Опубликована: Март 28, 2025

The convergence of Artificial Intelligence (AI) and Cloud Computing has ushered in a new era innovation across various industries, including healthcare. AI, with its ability to analyze vast datasets, identify patterns, make intelligent decisions, offers transformative potential for improving patient outcomes enhancing healthcare efficiency. Computing, on the other hand, provides scalable flexible infrastructure storing, processing, accessing data, enabling seamless collaboration among professionals development innovative applications. This overview presents comprehensive intersection AI healthcare, exploring their applications, benefits, challenges, ethical considerations. survey will delve into aspects cloud computing adoption usage, challenges opportunities, future trends, expectations.

Язык: Английский

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Deep-Hill: An Innovative Cloud Resource Optimization Algorithm by Predicting SaaS Instance Configuration Using Deep Learning

IEEE Access, Год журнала: 2024, Номер 12, С. 92573 - 92584

Опубликована: Янв. 1, 2024

The integration of Artificial Intelligence (AI) services within the framework Software-as-a-Service (SaaS) cloud architecture has significantly permeated our everyday routines. These AI diverge from traditional applications by offering a more personalized user experience. That is why predefined instance configuration not an optimal approach for these applications. challenge further compounded unpredictable nature demand, making resource allocation to instances complex task. This paper introduces innovative algorithm, termed Deep-Hill, designed enhance through precise prediction SaaS configurations. It combination 5-layer Deep Neural Network (DNN) and Hill-Climbing algorithm. unique classifies in one five classes with 96.33% accuracy, 90.83% precision, 90.96% recall, 90.86% F1-score. On average, it reduces number active hosts four, contributing 13.33% less power consumption. remarkable performance Deep-Hill algorithm underscores its potential set new benchmark optimization resources. paves way cost-effective applications, marking significant step forward evolution computing.

Язык: Английский

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Flamingo Search Sailfish Optimizer Based SqueezeNet for Detection of Breast Cancer Using MRI Images

Cancer Investigation, Год журнала: 2024, Номер unknown, С. 1 - 24

Опубликована: Сен. 20, 2024

Breast cancer with increased risk in women is identified Magnetic Resonance Imaging (Breast MRI) and this helps evaluating treatment therapies. MRI time time-consuming process that involves the assessment of current imaging. This research work depends on detection breast at earlier stages. Among various cancers, occurs larger accounts for almost 30% estimated cases. In research, many steps are followed like pre-processing, segmentation, augmentation, extraction features, detection. Here, median filter utilized as well segmentation after which done by Psi-Net. Moreover, augmentation shearing, translation, cropping segmentation. Also, segmented image tends to feature extraction, where features shape Completed Local Binary Pattern (CLBP), Pyramid Histogram Oriented Gradients (PHOG), statistical extracted. Finally, detected using DL model, SqueezeNet. newly devised Flamingo Search SailFish Optimizer (FSSFO) used training Psi-Net Furthermore, FSSFO combination both Algorithm (FSA) (SFO).

Язык: Английский

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Evolutionary RNN framework for Precise Lung Nodule Detection from CT Scans

International Journal of Advanced Research in Science Communication and Technology, Год журнала: 2024, Номер unknown, С. 180 - 185

Опубликована: Фев. 6, 2024

Radiologists find it challenging and time-consuming to recognize evaluate nodules of lung using CT scans that are malignant. Because this, early growth prediction is necessary for the inquiry technique, which raises likelihood treatment will be successful. Computer-aided diagnostic (CAD) tools have been used help with this issue. The primary goal work identify if cancerous or not deliver more accurate results. RNN [Recurrent] a type neural network model includes feedback loop. In paper, evolutionary algorithms examined MATLAB Tool, including Grey Wolf Optimization Algorithm Recurrent Neural Network (RNN) Techniques. Additionally, statistical characteristics generated in comparison other RNNs Particle Swarm (PSO) Genetic (GA) combinations. Comparing suggested approach state-of-the-art techniques, yielded results extremely high accuracy, sensitivity, specificity, precision. past few years, there has substantial increase field feature selection due their simplicity potential global search capabilities. solutions outperformed classical approaches employed across various fields, showing excellent Determining whether become malignant made easier identification.

Язык: Английский

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