Thermographic Images-Based Lung Cancer Detection Using a Convolutional Neural Network Algorithm DOI
Anindita Saha, Rakesh Kumar Yadav

Published: June 23, 2023

The most recent statistics show that lung cancer is the prevalent type of worldwide, claiming lives an estimated untold number people every year. Accurate diagnosis and early illness identification can increase likelihood effective treatment decrease death. In fact, help prevent its spread protect prematurely ill from getting it. Machine learning, which uses algorithms capable locating identifying patterns in images, enables cancer. this study, we propose a computer-aided diagnostic (CAD) method for patient image database. For improved classification outcomes to identify greater success rate categorization mammography, analyses assess convolution neural network (CNN) on three cross-folds, k = 2, 3 5, various epochs (0, 1, 3, 4, 5).

Language: Английский

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets DOI Creative Commons
Xingsi Xue,

C. Seelammal,

Ghaida Muttashar Abdulsahib

et al.

Bioengineering, Journal Year: 2023, Volume and Issue: 10(3), P. 363 - 363

Published: March 16, 2023

Recently, various methods have been developed to identify COVID-19 cases, such as PCR testing and non-contact procedures chest X-rays computed tomography (CT) scans. Deep learning (DL) artificial intelligence (AI) are critical tools for early accurate detection of COVID-19. This research explores the different DL techniques identifying pneumonia on medical CT radiography images using ResNet152, VGG16, ResNet50, DenseNet121. The ResNet framework uses scan with accuracy precision. automates optimum model architecture training parameters. Transfer approaches also employed solve content gaps shorten duration. An upgraded VGG16 deep transfer is applied perform multi-class classification X-ray imaging tasks. Enhanced has proven recognize three types radiographic 99% accuracy, typical pneumonia. validity performance metrics proposed were validated publicly available data sets. suggested outperforms competing in diagnosing primary outcomes this result an average F-score (95%, 97%). In event healthy viral infections, more efficient than existing methodologies coronavirus detection. created appropriate recognition pre-training. traditional strategies categorization illnesses.

Language: Английский

Citations

36
An Intelligent Buffalo-Based Secure Edge-Enabled Computing Platform for Heterogeneous IoT Network in Smart Cities DOI Creative Commons

Reyazur Rashid Irshad,

Shahid Hussain,

Ihtisham Hussain

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 69282 - 69294

Published: Jan. 1, 2023

The Internet of Things (IoT) based Smart city applications are the latest technology-driven solutions designed to collect and analyze data enhance quality life for urban residents by creating more sustainable, efficient, connected communities. Communication nodes networked independently monitor circumstances, where they require being energy-efficient securing, improve performance sustainable cities. Because, enormous number devices makes smart cities application vulnerable many security breaches, all which have serious ramifications a its residents' safety, well-being, economic development. Low-powered sensors limitations in terms battery life, short transmission range, considerations, despite fact that combination edge computing Green IoT considerably enhances network processing storage. Consequently, it is necessary implement an advanced approach provide energy resources with secure Therefore, this research proposes Intelligent Buffalo-based Secure Edge-enabled Computing (IB-SEC) framework platform, aims communication efficiency, reliability, minimizing latency consumption transmission. developed IB-SEC platform utilizes African Buffalo Optimization (ABO) algorithm Distributed Hash function-based security, reliability IoT-based networks. This leverages capabilities MAC protocols achieve goals, implementing encryption, authentication, access control mechanisms ensure wireless secure, protected against unauthorized access. Overall, provides networks enable applications. Moreover, enables integration heterogeneous devices, sensors, systems effectively managed adapted Median Access Control (MAC) protocols. implemented MATLAB validated through cutting-edge algorithms improved throughput, reduced consumption.

Language: Английский

Citations

15
Modelling and Analysis of Hybrid Transformation for Lossless Big Medical Image Compression DOI Creative Commons
Xingsi Xue, Raja Marappan,

Sekar Kidambi Raju

et al.

Bioengineering, Journal Year: 2023, Volume and Issue: 10(3), P. 333 - 333

Published: March 6, 2023

Due to rapidly developing technology and new research innovations, privacy data preservation are paramount, especially in the healthcare industry. At same time, storage of large volumes medical records should be minimized. Recently, several types on lossless medically significant compression various steganography methods have been conducted. This develops a hybrid approach with advanced steganography, wavelet transform (WT), ensure storage. focuses preserving patient through enhanced security optimized images that allow pharmacologist store twice as much information space an extensive repository. Safe storage, fast image service, minimum computing power main objectives this research. work uses smooth knight tour (KT) algorithm embed into discrete WT (DWT) protect shield images. In addition, packet is used minimize memory footprints maximize efficiency. JPEG formats' ratio percentages slightly higher than those PNG formats. When size increases, is, for high-resolution images, lies between 7% 7.5%, percentage 30% 37%. The proposed model increases expected compared other models. average 7.8% 8.6%, 35% 60%. Compared state-of-the-art methods, results greater without compromising quality. Reducing makes them easier process allows many saved archives.

Language: Английский

Citations

13
Predicting DoS and DDoS attacks in network security scenarios using a hybrid deep learning model DOI Creative Commons
Azhar F. Al-zubidi, Alaa Kadhim Farhan,

Sayed M. Towfek

et al.

Journal of Intelligent Systems, Journal Year: 2024, Volume and Issue: 33(1)

Published: Jan. 1, 2024

Abstract Network security faces increasing threats from denial of service (DoS) and distributed (DDoS) attacks. The current solutions have not been able to predict mitigate these with enough accuracy. A novel effective solution for predicting DoS DDoS attacks in network scenarios is presented this work by employing an model, called CNN-LSTM-XGBoost, which innovative hybrid approach designed intrusion detection security. system applied analyzed three datasets: CICIDS-001, CIC-IDS2017, CIC-IDS2018. We preprocess the data removing null duplicate data, handling imbalanced selecting most relevant features using correlation-based feature selection. evaluated accuracy, precision, F 1 score, recall. achieves a higher accuracy 98.3% 99.2% CICIDS2017, 99.3% CIC-ID2018, compared other existing algorithms. also reduces overfitting model important features. This study shows that proposed efficient attack classification.

Language: Английский

Citations

5
Medical Imaging and Image Processing DOI Creative Commons
Yudong Zhang, Zhengchao Dong

Technologies, Journal Year: 2023, Volume and Issue: 11(2), P. 54 - 54

Published: April 5, 2023

Medical imaging (MI) [...]

Language: Английский

Citations

11
DKCN-Net: Deep Kronecker Convolutional Neural Network-based lung disease detection with Federated Learning DOI
Alice Meda, Leema Nelson, Mukta Jagdish

et al.

Computational Biology and Chemistry, Journal Year: 2025, Volume and Issue: 116, P. 108376 - 108376

Published: Feb. 8, 2025

Language: Английский

Citations

0
Comparative Analysis of CNN, Xception and Inceptionv3 for Classifying Tuberculosis, Pneumonia Normal Chest X-ray Images DOI

Lakshmi Narasimham Chennareddy,

Madhavi Katamaneni,

Raghu Varma Revalamadugu

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

1
Applying Machine Learning & Knowledge Discovery to Intelligent Agent-Based Recommendation for Online Learning Systems DOI

S Murugesan,

N. Bharathiraja,

K Pradeepa

et al.

Published: March 17, 2023

E-learning is one of the favorite jobs where all learners can learn through different online sites to update their knowledge acquisition. Though learning supports several benefits, there are many challenges be considered, for example, information resources, quality search, accessing correct information, getting search results within a reasonable computing time, etc. This research develops new natural language processing (NLP) intelligent and recommender using learner's profile semantic analysis history at times. The clustering strategy applied, so system learns automatically, characteristics analyzed periodically. simulation proposed compared with state-of-the-art techniques provide minimized error rates selection courses recommendations recommendation accuracy in (98.2%, 98.9%). MAE lies between (0.12, 0.35) considered clusters. Compared other methods, method works well clusters sizes 10, 20, 30, 40, 50.

Language: Английский

Citations

3
Synergistic Fusion of U-Net and DeepLabV3+ for Enhanced Lung Disease Segmentation: A Comprehensive Evaluation and Performance Analysis DOI

Rakhi Gangal,

Avneesh Kumar, Prashant Johri

et al.

Published: Feb. 9, 2024

Unet and DeepLabV3+ join forces in the realm of lung disease screening. Unet's knack for identifying tricky features pairs up with DeepLabV3+'s extensive view, enhancing our fight against disease. What we get is better exactness mapping these illnesses. Doctors can now spot early plan perfect treatment. Sure, it needs a lot data processing power, but it's small price big leap detection. This blend propels us to future diagnosis, marking powerful union deep learning medical imaging tech. model gets right 97.00% time when classifying pixels. The Dice Jaccard index, 90.8% 83.31% respectively, prove decent job at spotting tuberculosis-related issues. Yet, 0.9 loss points some hiccups during training. DeepLabV3+, on other hand, outdoes whopping 98.27% accuracy. Its score index 98.18% 96.43% showcase great potential finer details essential tuberculosis X-ray segmentation. delivers excellent results due low 0.14. shows its strong ability In all areas, this tool stands out. Especially, shines analysis tuberculosis, challenging task. quality makes aid advancing study pictures.

Language: Английский

Citations

0
Smart city energy efficient data privacy preservation protocol based on biometrics and fuzzy commitment scheme DOI Creative Commons
Vincent Omollo Nyangaresi, Zaid Ameen Abduljabbar,

Keyan Abdul-Aziz Mutlaq

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 13, 2024

Abstract Advancements in cloud computing, flying ad-hoc networks, wireless sensor artificial intelligence, big data, 5th generation mobile network and internet of things have led to the development smart cities. Owing their massive interconnectedness, high volumes data are collected exchanged over public internet. Therefore, messages susceptible numerous security privacy threats across these open channels. Although many techniques been designed address this issue, most them still vulnerable attacks while some deploy computationally extensive cryptographic operations such as bilinear pairings blockchain. In paper, we leverage on biometrics, error correction codes fuzzy commitment schemes develop a secure energy efficient authentication scheme for This is informed by fact that biometric cumbersome reproduce hence side-channeling thwarted. We formally analyze our protocol using Burrows–Abadi–Needham logic logic, which shows achieves strong mutual among communicating entities. The semantic analysis it mitigates de-synchronization, eavesdropping, session hijacking, forgery side-channeling. addition, its formal demonstrates under Canetti Krawczyk attack model. terms performance, shown reduce computation overheads 20.7% state-of-the-art protocols.

Language: Английский

Citations

0