Arnab Kumar Maji

Bio: Arnab Kumar Maji is an academic researcher from North Eastern Hill University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 5, co-authored 48 publications receiving 97 citations.

Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

Abstract: The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems.

Plant Disease Identification Using a Novel Convolutional Neural Network

Abstract: The timely identification of plant diseases prevents the negative impact on crops. Convolutional neural network, particularly deep learning is used widely in machine vision and pattern recognition task. Researchers proposed different deep learning models in the identification of diseases in plants. However, the deep learning models require a large number of parameters, and hence the required training time is more and also difficult to implement on small devices. In this paper, we have proposed a novel deep learning model based on the inception layer and residual connection. Depthwise separable convolution is used to reduce the number of parameters. The proposed model has been trained and tested on three different plant diseases datasets. The performance accuracy obtained on plantvillage dataset is 99.39%, on the rice disease dataset is 99.66%, and on the cassava dataset is 76.59%. With fewer number of parameters, the proposed model achieves higher accuracy in comparison with the state-of-art deep learning models.

FT-SDN: A Fault-Tolerant Distributed Architecture for Software Defined Network

Abstract: The traditional Software Defined Network (SDN) architecture is based on single controller in the Control Plane. Therefore, network functioning become highly dependent on the performance of the single controller in the Control Plane, which is undesirable for any reliable application. Despite many advantages of SDN, its deployment in the practical field is restricted since reliability and fault-tolerance capabilities of the system are not satisfactory. To overcome these difficulties of SDN, (FT-SDN) architecture has been proposed. The proposed architecture consists of a simple and effective distributed Control Plane with multiple controllers. FT-SDN uses a synchronized mechanism to periodically update the controller’s state within themselves. In case of failure, FT-SDN has the ability to select another working controller based on the distance and delays among different network entities. The performance of the FT-SDN architecture was examined with respect to different specifications in the presence of faults. Experimentation was done in simulation where results were found to be satisfactory.

Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Abstract: This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age

Abstract: Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis of brain tumors. Previous studies have focused on the classification of normal (nontumorous) or abnormal (tumorous) brain MRIs using methods such as Support Vector Machine (SVM) and AlexNet. In this paper, deep learning architectures are used to classify brain MRI images into normal or abnormal. Gender and age are added as higher attributes for more accurate and meaningful classification. A deep learning Convolutional Neural Network (CNN)-based technique and a Deep Neural Network (DNN) are also proposed for effective classification. Other deep learning architectures such as LeNet, AlexNet, ResNet, and traditional approaches such as SVM are also implemented to analyze and compare the results. Age and gender biases are found to be more useful and play a key role in classification, and they can be considered essential factors in brain tumor analysis. It is also worth noting that, in most circumstances, the proposed technique outperforms both existing SVM and AlexNet. The overall accuracy obtained is 88% (LeNet Inspired Model) and 80% (CNN-DNN) compared to SVM (82%) and AlexNet (64%), with best accuracy of 100%, 92%, 92%, and 81%, respectively.

Linguistic Survey of India

Abstract: THIS year there have been completed two very notable works in the field of linguistic science: one is the “New English Dictionary,” finished after seventy years of labour; the other is the great “Linguistic Survey of India,” of which the last volume to be published is now before us. No scholar can work at any problem connected with the history of English without constant appeal to the Dictionary, and no scholar can work at any problem connected with the languages of India without constant appeal to the Survey. Indeed, we can scarcely recall during the last fifteen years an article or book on the history of any of these languages (and shortly it will be seen how numerous and diverse they are) in which reference has not been made to the facts set forth in the Survey, often for the first time. Linguistic Survey of India. By Sir George Abraham Grierson. Vol. 1, Part 1: Introductory. Pp. xviii + 517. (Calcutta: Government of India Central Publication Branch; London: High Commissioner for India, 1927.) 11.12 rupees; 19s.

Blockchain‐based security aspects in heterogeneous Internet‐of‐Things networks: A survey

Abstract: Blockchain is a key technology that enables cryptocurrencies such as Bitcoin, Litecoin, etc. In recent years, researchers have ventured into tapping the potential of blockchain‐based ecosy...

AlexNet Convolutional Neural Network for Disease Detection and Classification of Tomato Leaf

Abstract: With limited retrieval of reserves and restricted capability in plant pathology, automation of processes becomes essential. All over the world, farmers are struggling to prevent various harm from bacteria or pathogens such as viruses, fungi, worms, protozoa, and insects. Deep learning is currently widely used across a wide range of applications, including desktop, web, and mobile. In this study, the authors attempt to implement the function of AlexNet modification architecture-based CNN on the Android platform to predict tomato diseases based on leaf image. A dataset with of 18,345 training data and 4,585 testing data was used to create the predictive model. The information is separated into ten labels for tomato leaf diseases, each with 64 × 64 RGB pixels. The best model using the Adam optimizer with a realizing rate of 0.0005, the number of epochs 75, batch size 128, and an uncompromising cross-entropy loss function, has a high model accuracy with an average of 98%, a strictness rate of 0.98, a recall value of 0.99, and an F1-count of 0.98 with a loss of 0.1331, so that the classification results are good and very precise.

An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging

Abstract: Alzheimer’s disease (AD) is a neurodegenerative disease that affects brain cells, and mild cognitive impairment (MCI) has been defined as the early phase that describes the onset of AD. Early detection of MCI can be used to save patient brain cells from further damage and direct additional medical treatment to prevent its progression. Lately, the use of deep learning for the early identification of AD has generated a lot of interest. However, one of the limitations of such algorithms is their inability to identify changes in the functional connectivity in the functional brain network of patients with MCI. In this paper, we attempt to elucidate this issue with randomized concatenated deep features obtained from two pre-trained models, which simultaneously learn deep features from brain functional networks from magnetic resonance imaging (MRI) images. We experimented with ResNet18 and DenseNet201 to perform the task of AD multiclass classification. A gradient class activation map was used to mark the discriminating region of the image for the proposed model prediction. Accuracy, precision, and recall were used to assess the performance of the proposed system. The experimental analysis showed that the proposed model was able to achieve 98.86% accuracy, 98.94% precision, and 98.89% recall in multiclass classification. The findings indicate that advanced deep learning with MRI images can be used to classify and predict neurodegenerative brain diseases such as AD.