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Article
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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Article
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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Article
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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Chapter and Conference Paper
Identifying Multiple Diseases on a Single Citrus Leaf Using Deep Learning Techniques
Deep learning techniques for classifying images into multiple classes have made significant strides in the past few years. Nevertheless, allocating multiple classes to a single image, as seen in object detecti...
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Chapter and Conference Paper
Lung Cancer Prediction Using DBSMOTE and SVM
Lung cancer is one of the leading causes of death nowadays. It has been found in older and younger people in recent years, and the figures are worrying. Detecting lung cancer in the early stage significantly i...
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Article
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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Article
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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Article
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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Article
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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Article
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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Article
Novel fuzzy clustering-based undersampling framework for class imbalance problem
The class imbalance problem occurs in various real-world datasets. Although it is considered that samples of the classes of a dataset are evenly distributed, in many cases, datasets are highly imbalanced. Clas...
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Chapter and Conference Paper
Plant Disease Classification Using Siamese Convolutional Neural Network
Through the years, plant diseases have been a consistent risk to food security. Hence, their rapid identification could significantly mitigate the economic losses around the world, also reducing the harmful ef...
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Chapter and Conference Paper
TLDC: Tomato Leaf Disease Classification Using Deep Learning and Image Segmentation
Deep learning (DL) has made significant progress in identifying and classifying plant diseases. The convolutional neural network (CNN) model was utilized to classify diseased and healthy tomato plant leaves fo...
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Article
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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Article
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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Chapter and Conference Paper
A Forecasting Technique for Powdery Mildew Disease Prediction in Tomato Plants
In the current scenario, plant disease detection is seeking attention from many agricultural scientists. Plant diseases are deeply influenced by the weather conditions, and each disease has its individual weat...
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Chapter and Conference Paper
State-Wise Analysis and Prediction of Covid-19 in India
Machine learning (ML) is an application of artificial intelligence (AI) and through ML, the system gets the capability of learning by refining from experience. Whereas deep learning, machine learning’s subsect...
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Chapter and Conference Paper
Classification and Activation Map Visualization of Banana Diseases Using Deep Learning Models
Machine learning, especially deep learning (DL), comprises a modern, recent technique to process the images and data, with promising outcomes and enormous potential. DL is acquiring prevalence as it proves its...
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Chapter and Conference Paper
Synthesis of KNN Algorithm in FPGA Technology
Machine learning is coming everywhere, and currently in every field, it is contributing in terms of different applications. Machine learning is a type of artificial intelligence (AI), which enables a program t...
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Article
Weight and bias initialization routines for Sigmoidal Feedforward Network
The success of the Sigmoidal Feedforward Networks in the solution of complex learning task can be attributed to their Universal Approximation Property. These networks are trained using non-linear iterative optimi...
