Lentil Plant Disease and Quality Assessment: A Detailed Dataset of High-Resolution Images for Deep Learning Research DOI Creative Commons
Eram Mahamud, Md Assaduzzaman, Shayla Sharmin

и другие.

Data in Brief, Год журнала: 2024, Номер 58, С. 111224 - 111224

Опубликована: Дек. 12, 2024

The Lentil, a vital legume globally cultivated, faces significant challenges from diseases like ascochyta blight, lentil rust, and powdery mildew. Ensuring optimal harvest timing effectively discerning healthy diseased plants are crucial for maintaining crop quality economic viability, particularly in regions such as Bangladesh. This paper introduces comprehensive dataset comprising high-resolution images of gathered meticulously over four months diverse locations across Bangladesh, under expert supervision. aims to support the development machine-learning models precise disease detection assessment cultivation. Potential applications include enhancing accuracy evaluation, improving packaging processes, thereby overall production efficiency. Agricultural researchers can utilize this advance computer vision deep learning managing yield outcomes. dataset's creation involved collaboration with domain experts ensure its relevance reliability agricultural research. By leveraging dataset, explore innovative approaches tackle farming, contributing sustainable practices food security. Moreover, serves valuable resource training testing machine algorithms tailored settings, facilitating advancements automated technologies. Ultimately, initiative empower stakeholders industry tools mitigate impact optimize practices, paving way more resilient efficient systems globally.

Язык: Английский

Enhanced Deep Learning Based Decision Support System for Kidney Tumour Detection DOI Creative Commons
Taha Etem, Mustafa Teke

BenchCouncil Transactions on Benchmarks Standards and Evaluations, Год журнала: 2024, Номер 4(2), С. 100174 - 100174

Опубликована: Июнь 1, 2024

Язык: Английский

Процитировано

1
GSC-DVIT: A vision transformer based deep learning model for lung cancer classification in CT images DOI

Durgaprasad Mannepalli,

K. T. Tan, Sivaneasan Bala Krishnan

и другие.

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 103, С. 107371 - 107371

Опубликована: Дек. 30, 2024

Язык: Английский

Процитировано

1
MLBFN optimized with Archimedes optimization Algorithm for SRCE DOI
K. Sathish Kumar,

Sridevi Sonaimuthu,

Navaneetha Rama Krishnan Alangudi Balaji

и другие.

Expert Systems with Applications, Год журнала: 2024, Номер 255, С. 124529 - 124529

Опубликована: Июнь 21, 2024

Язык: Английский

Процитировано

0
Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things DOI

A. K.,

Suchithra Kumari M H,

Sayyad Jilani

и другие.

Smart Science, Год журнала: 2024, Номер unknown, С. 1 - 15

Опубликована: Авг. 12, 2024

Nowadays, the network intrusion and cyberattack have emerged as two main issues with Internet of Things (IoT) applications. The existing methods for preventing detecting intrusions are limited in many ways, making it impossible to accurately identify any kind attack occurring within traffic. A number machine learning-based that attains poor performance multiple class categorization accuracy provided by researchers. This research presents Data-Driven Intrusion Detection System utilizing Optimized Bayesian Regularization-Back Propagation Neural Network (DIDS-BRBPNN-BBWOA-IoT) overcome these issues. input data is taken from TON_IoT Dataset. balancing training dataset enhanced using Class decomposition synthetic minority oversampling method (CDSMOTE). Then, pre-processed Variational Bayesian-based Maximum Correntropy Cubature Kalman Filtering (VBMCCKF) noise removal enhancement. preprocessed output given into feature extraction extract features Dual-Tree Biquaternion Wavelet Transform (DTBWT). extracted fed (BRBPNN) which detects Ransomware, Password attack, Scanning, Denial Service (DoS), Distributed (DDoS), Data injection, Backdoor, Cross-Site Scripting (XSS), Man-In-The-Middle (MITM). In general, BRBPNN does not show optimization adaption determine optimal parameter appropriate detection. Hence, Binary Black Widow Optimization Algorithm (BBWOA) proposed this manuscript improve classifier precisely. DIDS-BRBPNN-BBWOA-IoT implemented Python. approach examined metrics like accuracy, precision, recall, f1-score, specificity, error rate; computation time, ROC. SAPVAEGAN-LCC-IR 18.44%, 26% ,and 29% greater accuracy; 26.55%, 24.12%, 27.22% recall compared MIDS-MIoT, AID-SDN-IoT, IID-LW-IoT techniques.

Язык: Английский

Процитировано

0
Automated lung cancer detection using novel genetic TPOT feature optimization with deep learning techniques DOI Creative Commons
Mohamed Hammad, Mohammed ElAffendi, Muhammad Asim

и другие.

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103448 - 103448

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

0
Feature Extraction Techniques for Patterned Images: A Systematic Literature Review DOI

Wenti Ayu Wahyuni,

Arief Setyanto, Ema Utami

и другие.

Опубликована: Сен. 12, 2024

Язык: Английский

Процитировано

0
Lentil Plant Disease and Quality Assessment: A Detailed Dataset of High-Resolution Images for Deep Learning Research DOI Creative Commons
Eram Mahamud, Md Assaduzzaman, Shayla Sharmin

и другие.

Data in Brief, Год журнала: 2024, Номер 58, С. 111224 - 111224

Опубликована: Дек. 12, 2024

The Lentil, a vital legume globally cultivated, faces significant challenges from diseases like ascochyta blight, lentil rust, and powdery mildew. Ensuring optimal harvest timing effectively discerning healthy diseased plants are crucial for maintaining crop quality economic viability, particularly in regions such as Bangladesh. This paper introduces comprehensive dataset comprising high-resolution images of gathered meticulously over four months diverse locations across Bangladesh, under expert supervision. aims to support the development machine-learning models precise disease detection assessment cultivation. Potential applications include enhancing accuracy evaluation, improving packaging processes, thereby overall production efficiency. Agricultural researchers can utilize this advance computer vision deep learning managing yield outcomes. dataset's creation involved collaboration with domain experts ensure its relevance reliability agricultural research. By leveraging dataset, explore innovative approaches tackle farming, contributing sustainable practices food security. Moreover, serves valuable resource training testing machine algorithms tailored settings, facilitating advancements automated technologies. Ultimately, initiative empower stakeholders industry tools mitigate impact optimize practices, paving way more resilient efficient systems globally.

Язык: Английский

Процитировано

0