Neutrosophic approach based intelligent system for automatic mango detection

Tripathi, Mukesh Kumar; Shivendra,

doi:10.1007/s11042-023-17037-7

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…They are trained on multiple datasets and their performance is evaluated. The paper aims to compare the performance of two different models, the random forest model [19] and the CNN model [20], specifically in the context of pothole identification. Evaluating the accuracy and other performance metrics across various datasets is a valuable approach to understand how well each model performs under different conditions.…”

Section: Proposed Systemmentioning

confidence: 99%

Pothole detection in bituminous road using convolutional neural network with transfer learning

Tripathi,

Baswaraj,

Deshmukh

et al. 2024

IJEECS

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<p>The challenges of road maintenance, particularly in detecting potholes and cracks, and the proposed method using transfer learning and convolutional neural networks (CNNs) are significant advancements in this domain. Transfer learning is particularly beneficial, as it allows leverage pre-trained models to enhance the performance of the pothole detection system. CNNs, with their ability to capture spatial hierarchies in data, are well-suited for image-based tasks like pothole detection. The potential applications of the suggested method for intelligent transportation systems (ITS) services, such as alerting drivers about real-time potholes, demonstrate we research’s practical implications. This contributes to road safety and aligns with the broader goals of innovative city initiatives and infrastructure management. Achieving a 96% accuracy rate is a significant result, indicating the robustness of the proposed approach. Using this information to assess initial maintenance needs in a road management system is forward-thinking. Overall, we work is a valuable contribution to intelligent transportation and infrastructure management, showcasing the potential of advanced machine-learning techniques for addressing critical issues in road maintenance.</p>

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Section: Proposed Systemmentioning

confidence: 99%

Pothole detection in bituminous road using convolutional neural network with transfer learning

Tripathi,

Baswaraj,

Deshmukh

et al. 2024

IJEECS

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show abstract

“…The authors proposed all in one image dehazing (AOD)-Net architecture for image dehazing [20], [21]. This end-to-end trainable network combines both global and local information for accurate dehazing.…”

mentioning

confidence: 99%

SEM and TEM images’ dehazing using multiscale progressive feature fusion techniques

Moorthy,

Tripathi,

Joshi

et al. 2024

IJEECS

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<p>We present a highly effective algorithm for image dehazing that leverages the valuable information within the hazy image to guide the haze removal process. Our proposed algorithm begins by employing a neural network that has been trained to establish a mapping between hazy images and their corresponding clear versions. This network learns to identify the shared structural elements and patterns between hazy and clear images through the training process. To enhance the utilization of guidance information from the generated reference image, we introduce a progressive feature fusion module that combines the features extracted from the hazy image and the reference image. Our proposed algorithm is an effective solution for image dehazing, as it capitalizes on the guidance information in the hazy appearance. By combining the strengths of deep learning, progressive feature fusion, and end-to-end training, we achieve impressive results in restoring clear images from hazy counterparts. The practical applicability of our algorithm is further validated by its success on benchmark data sets and real-world SEM and TEM images.</p>

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Multimodal emotion recognition model via hybrid model with improved feature level fusion on facial and EEG feature set

Singh,

Tripathi,

Patil

et al. 2024

Multimed Tools Appl

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Neutrosophic approach based intelligent system for automatic mango detection

Cited by 3 publications

References 25 publications

Pothole detection in bituminous road using convolutional neural network with transfer learning

Pothole detection in bituminous road using convolutional neural network with transfer learning

SEM and TEM images’ dehazing using multiscale progressive feature fusion techniques

Multimodal emotion recognition model via hybrid model with improved feature level fusion on facial and EEG feature set

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