Identification of mango variety using near infrared spectroscopy

Tripathi, Mukesh Kumar; Reddy, Praveen Kumar; Neelakantappa, M.; Andhare, Chetan Vikram; Shivendra, Shivendra

doi:10.11591/ijeecs.v31.i3.pp1776-1783

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…CNNs are a type of artificial neural network (ANN) renowned for their exceptional performance in tasks related to categorization and sensing, particularly in the domains of feature sensing, object sensing, and image object sensing [23], [24]. The experiment conducted in our study involves the use of a pothole dataset and real-time car video.…”

Section: Convolutional Neural Networkmentioning

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: Convolutional Neural Networkmentioning

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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“…Tripathi et al [14] addresses the challenge of estimating visibility in adverse weather conditions using a single image. The research aims to develop a method that can accurately predict visibility levels based…”

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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