13 Result(s)
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Anuradha Chug
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modSwish: a new activation function for neural network
The activation functions are extremely important to neural networks since they are responsible for learning the abstract characteristics of the data through nonlinear modification. The paper presents a new act...
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Understanding the effect of batch refactoring on software quality
Developers aim to create software with the least possible flaws and good quality. Hence, they apply a sequence of program transformation operations known as refactoring to create such software. Refactoring is ...
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Meteorological Factor-Based Tomato Early Blight Prediction Using Hyperparameter Tuning of Intelligent Classifiers
Early blight is a severe disease which affects several plant species, including tomato plants. Weather parameters such as temperature, leaf wetness, soil moisture, and relative humidity play a vital role in th...
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Revisiting activation functions: empirical evaluation for image understanding and classification
In this paper, the authors have devised four novel activation functions by coupling and combining a few existing functions implemented with four standard CNN architectures namely VGG19, ResNet50, InceptionV3, ...
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Plant Disease Detection and Severity Assessment Using Image Processing and Deep Learning Techniques
Efficient plant disease detection and severity assessment are crucial not just for agricultural purposes but also for global health, economics, as well as ecological sustainability. With the help of innovative...
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Plant Foliage Disease Diagnosis Using Light-Weight Efficient Sequential CNN Model
The Precise and prompt identification of plant pathogens is essential to keep agricultural losses as low as possible. In recent time, deep convolution neural networks have seen an exponential growth in their u...
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PDS-MCNet: a hybrid framework using MobileNetV2 with SiLU6 activation function and capsule networks for disease severity estimation in plants
Advanced technologies like deep learning have been widely implemented in various agricultural applications, including disease severity estimation. In this study, the authors have leveraged the computational ca...
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Classification of crop leaf diseases using image to image translation with deep-dream
Crop diseases are one of the primary triggers of yield devastation. As a result, early detection of crop diseases is critical to avert crop losses. In this study, a Deep-Dream (DD) based crop leaf disease dete...
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A novel framework for image-based plant disease detection using hybrid deep learning approach
The agriculture sector contributes significantly to the economic growth of a country. However, plant diseases are one of the leading causes of crop destruction that decreases the quality and quantity of agricu...
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Fractional mega trend diffusion function-based feature extraction for plant disease prediction
Plant diseases can severely degrade the quality and productivity of any crop. Hence, an automated forecasting model can be developed to help the farmers and agricultural experts for early detection and on-time...
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A machine learning-based spray prediction model for tomato powdery mildew disease
Powdery mildew is the most commonly observed disease of tomato plants, which affects its quality and productivity. On-time treatment with an optimized amount of fungicides spray can improve the yield and quali...
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Benchmarking framework for class imbalance problem using novel sampling approach for big data
The traditional techniques of machine learning always need to be strengthened for dealing with cosmic nature of big data for systematic and methodical learning. The unbalanced distribution of classes in big da...
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Application of Group Method of Data Handling model for software maintainability prediction using object oriented systems
Object-oriented methodology has emerged as most prominent in software industry for application development. Maintenance phase begins once the product is delivered and by software maintainability we mean the ea...
