Modified Deep-Convolution Neural Network Model for Flower Images Segmentation and Predictions

Jaiswal, Varshali; Sharma, Varsha; Bisen, Dhananjay

doi:10.1007/s11042-023-16530-3

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…CNNs learn to automatically extract low-level characteristics such as edges and textures in early layers before progressing to more abstract and high-level features in later layers. Pooling layers reduce feature maps to improve translation invariance, whilst activation functions add nonlinearity to the network [26]. CNNs have transformed computer vision tasks, reaching cuttingedge performance in picture classification, object detection, and segmentation, among other things, and have found broad use in a variety of fields like as autonomous vehicles, medical imaging, and facial recognition.…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Environmental Sustainability in the Age of Deep Learning: Balancing Technological Advancement with Ecological Responsibility

Manesh Patil

2024

jes

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The convergence of technological innovation, particularly deep learning (DL), with the importance of responsibility for the environment in achieving environmental sustainability. Deep learning (DL) offers to improve sustainability in different areas. This paper discusses DL breakthroughs and their applications in accomplishing SDGs, renewable energy, and environmental health. This discovers problems in reconciling technological innovation with caring for the environment by investigating the uses of deep learning in diverse areas and measuring their environmental implications. Furthermore, it explores CNN and LSTM techniques in Deep learning for incorporating environmental factors into the development, application, benefits and challenges of DL technologies to promote sustainability. This study aims to provide insights and recommendations for creating a harmonious link between technical advancement and ecological responsibility in the pursuit of environmental sustainability by conducting a comprehensive review of existing literature. There are three indicators: MAPE, RMSE, and MAE. The MAPE, RMSE, and MAE results are provided based on 7.5, 15, and 30 minutes, indicating low forecast accuracy.

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Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Environmental Sustainability in the Age of Deep Learning: Balancing Technological Advancement with Ecological Responsibility

Manesh Patil

2024

jes

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“…In this study, we chose the Canny edge detection algorithm [23] to extract the features of the conveyor belt bilateral edges. The Canny edge detection algorithm can be divided into the following four steps [24]:…”

Section: Belt Edge Feature Extraction (1) Canny Edge Detectionmentioning

confidence: 99%

Research on Belt Deviation Fault Detection Technology of Belt Conveyors Based on Machine Vision

Wu,

Wang,

Tian

et al. 2023

Machines

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Traditional belt deflection detection devices for underground belt conveyors in coal mines have problems, such as their single function, poor fault location and analysis accuracy, low automation level, and low reliability. In order to solve the defects of traditional detection devices, the belt deviation faults of the underground belt conveyor transport process require to be detected effectively and reliably. This paper proposes a belt deviation detection method based on machine vision. This method makes use of a global adaptive high dynamic range imaging method to complete the brightness enhancement processing of the underground image. Then the straight-line features of the conveyor belt edges are extracted using Canny edge detection and the Hough transform algorithm. In addition, a dual-baseline localization judgment method is proposed to realize the identification of band bias faults. Finally, a test bench for belt conveyor deviation was built. Testing experiments for different deviations were conducted. The accuracy of the tape deviation detection reached 99.45%. The method proposed in this study improves the reliability of belt deviation fault detection of underground belt conveyors in coal mines and has wide application prospects in the field of coal mining.

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“…The introduction of Convolutional Neural Networks (CNNs) has transformed how medical pictures are evaluated and interpreted [2]. CNNs, a type of deep learning algorithm inspired by the human brain's visual cortex, have shown an extraordinary ability to automatically learn and extract relevant characteristics from images [3]. This has opened up new possibilities for using AI to help diagnose medical issues based on imaging data [4].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Medical Images Using Convolutional Neural Networks

Yogita K. Desai

2024

jes

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Medical image diagnosis using Convolutional Neural Networks (CNNs) has emerged as a viable way to improve the accuracy and efficiency of disease identification and categorization in clinical settings. In this study, they look at how CNNs can be used to diagnose lung nodules from chest X-ray pictures, to provide insights into the technology's performance and future clinical applications. A dataset of 10,000 tagged chest X-ray pictures showing both benign and malignant lung nodules was obtained and preprocessed using standard methods. The dataset was used to construct and train a proprietary CNN architecture, which was then rigorously evaluated on distinct training, validation, and test sets. The CNN model showed good accuracy (94.8%), sensitivity (92.1%), specificity (96.5%), precision, recall, F1 score, and area under the ROC curve (AUC), indicating its robustness and generalization ability. These findings show that CNN-based diagnostic tools may help radiologists and physicians discover and diagnose lung cancer earlier, improving patient outcomes and optimizing healthcare delivery. However, difficulties such as interpretability, data privacy, and regulatory approval must be addressed before CNNs can be fully utilized in medical imaging. This study emphasizes CNNs' transformative significance in diagnostic medicine and the necessity for additional research and development to realize their full potential in clinical practice.

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Modified Deep-Convolution Neural Network Model for Flower Images Segmentation and Predictions

Cited by 4 publications

References 33 publications

Environmental Sustainability in the Age of Deep Learning: Balancing Technological Advancement with Ecological Responsibility

Environmental Sustainability in the Age of Deep Learning: Balancing Technological Advancement with Ecological Responsibility

Research on Belt Deviation Fault Detection Technology of Belt Conveyors Based on Machine Vision

Diagnosis of Medical Images Using Convolutional Neural Networks

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