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

et al.

Data in Brief, Journal Year: 2024, Volume and Issue: 58, P. 111224 - 111224

Published: Dec. 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.

Language: Английский

A benchmark of deep learning approaches to predict lung cancer risk using national lung screening trial cohort DOI Creative Commons
Yifan Jiang, Leyla Ebrahimpour, Philippe Desprès

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 11, 2025

Deep learning (DL) methods have demonstrated remarkable effectiveness in assisting with lung cancer risk prediction tasks using computed tomography (CT) scans. However, the lack of comprehensive comparison and validation state-of-the-art (SOTA) models practical settings limits their clinical application. This study aims to review analyze current SOTA deep for (malignant-benign classification). To evaluate our model's general performance, we selected 253 out 467 patients from a subset National Lung Screening Trial (NLST) who had CT scans without contrast, which are most commonly used, divided them into training test cohorts. The were preprocessed 2D-image 3D-volume formats according nodule annotations. We evaluated ten 3D eleven 2D models, pretrained on large-scale general-purpose datasets (Kinetics ImageNet) radiological (3DSeg-8, nnUnet RadImageNet), performance. Our results showed that 3D-based generally perform better than models. On cohort, best-performing model achieved an AUROC 0.86, while best reached 0.79. lowest AUROCs 0.70 0.62, respectively. Furthermore, pretraining image did not show expected performance advantage over datasets. Both can handle effectively, although superior competitors. findings highlight importance carefully selecting architectures prediction. Overall, these important implications development integration DL-based tools screening.

Language: Английский

Citations

1
Intrusion detection and secure data storage in the cloud were recommend by a multiscale deep bidirectional gated recurrent neural network DOI

B. C. Preethi,

R. Vasanthi,

G. Sugitha

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 255, P. 124428 - 124428

Published: June 5, 2024

Language: Английский

Citations

4
PKMT-Net: A pathological knowledge-inspired multi-scale transformer network for subtype prediction of lung cancer using histopathological images DOI

Zhilei Zhao,

Shuli Guo,

Lina Han

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 106, P. 107742 - 107742

Published: Feb. 21, 2025

Language: Английский

Citations

0
Optimized theory-guided convolutional neural network for lung cancer classification using CT images with advanced FPGA implementation DOI

S. Manikandan,

P. Karthigaikumar

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 106, P. 107719 - 107719

Published: Feb. 22, 2025

Language: Английский

Citations

0
Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning DOI
Asadullah Shaikh, Samina Amin, Muhammad Ali Zeb

et al.

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 186, P. 109703 - 109703

Published: Jan. 24, 2025

Language: Английский

Citations

0
Flat Window Filter-Based Sparse Fast Fourier Transform Architecture Design for MIMO-OF DM Communication System DOI

A. Manimaran,

D. Prabhakar,

K. G. Revathi

et al.

IETE Journal of Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 9

Published: Feb. 23, 2025

Language: Английский

Citations

0
A Sensor Data-Driven Fault Diagnosis Method for Automotive Transmission Gearboxes Based on Improved EEMD and CNN-BiLSTM DOI Open Access
Youhong Xu, Hui Wang,

Feng Xu

et al.

Processes, Journal Year: 2025, Volume and Issue: 13(4), P. 1200 - 1200

Published: April 16, 2025

With the rapid development of new energy vehicle technologies, higher demands have been placed on fault diagnosis for automotive transmission gearboxes. To address poor adaptability traditional methods under complex operating conditions, this paper proposes a sensor data-driven method based improved ensemble empirical mode decomposition (EEMD) combined with convolutional neural networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The incorporates dynamic noise adjustment mechanism, allowing amplitude to adapt characteristics signal. This improves stability accuracy signal decomposition, effectively reducing instability error accumulation associated fixed-amplitude white in EEMD. By combining CNN BiLSTM modules, approach achieves efficient feature extraction modeling. First, vibration signals gearbox different states are collected via sensors, an EEMD is employed decompose signals, removing background nonstationary components extract diagnostically significant intrinsic functions (IMFs). Then, utilized features from IMFs, deeply mining their spatiotemporal characteristics, while captures temporal sequence dependencies enhancing comprehensive modeling nonlinear features. combination these two enables adaptation achieving accurate classification identification multiple modes. Results indicate that proposed highly robust identifying modes, significantly exceeding performance conventional isolated network models. provides intelligent solution

Language: Английский

Citations

0
Research on the Optimization Method of Project-Based Learning Design for Chinese Teaching Based on Interference-Tolerant Fast Convergence Zeroing Neural Network DOI Creative Commons

Weihua Bai,

Guoli Geng,

Xuan Fu

et al.

International Journal of Computational Intelligence Systems, Journal Year: 2024, Volume and Issue: 17(1)

Published: July 4, 2024

Abstract The system known as project-based learning, which is applied to specific courses without compromising the coverage of necessary technical material, uses projects drive knowledge. plan and implementation learning in Chinese teaching a major project, embraces undergraduate creativity places an emphasis on real-world, open-ended are discussed this paper. In paper, research optimization method design for based optimized interference-tolerant fast convergence zeroing neural network (PBLD-ITFCZNN-BRO). It consists three stages, import phase, main stage evaluation stage. initial teacher separated students groups before lecture make sure that every group poses various traits, with some strong leadership skills hands-on skills. second phase PBL procedure helped transform what primarily passive environment (taking notes, listening, sitting) into more dynamic, student-centered, interactive one. Students presented data, articulated their concepts, then approaches problem-solving during step. teachers concluded by summarizing. performance proposed PBLD-ITFCZNN-BRO approach contains 15.26%, 20.42% 21.27% greater accuracy, 15.61%, 17.50% 20.24% precision rate, compared Investigation Computer Network Technology New Media Problem-Basis Learning Teaching Mode (CNT-PBLTM), Model Basis application Deep Physical Education Classroom Integrating Production (PBL-DL-PEC), Interdisciplinary learning: experiences reflections from electronic engineering at china (PBL-EEC) techniques, respectively.

Language: Английский

Citations

1
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, Journal Year: 2024, Volume and Issue: 4(2), P. 100174 - 100174

Published: June 1, 2024

Language: Английский

Citations

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

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 103, P. 107371 - 107371

Published: Dec. 30, 2024

Language: Английский

Citations

1