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Subject: search.filters.subject.Convolutional Neural Networks (CNN)

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    Lightweight convolutional neural network architecture implementation using TensorFlow lite
    (Springer, 2023-06) Asati, Abhijit
    Recently, with the increase in the precision of convolutional neural networks (CNN) on a wide variety of classification and recognition tasks, the demand for their deployment has dramatically increased. Even the focus is on lightweight, faster, and low-power implementations. In this paper, we have implemented a CNN model onto an embedded platform, ‘Raspberry Pi 4-Model B edge computing system (RP4-BECS)’. This CNN model was initially trained and verified in MATLAB and then implemented on the Machine Learning (ML) framework to generate a TensorFlow lite (TF-lite) flat buffer format. This implementation offers a reduced size of models with good prediction accuracy and lesser inference time as compared with the available literature. We attempted three trials for all the digits from 0 to 9 to evaluate average prediction accuracy and average inference time. An average prediction accuracy of 99.32% and average inference time of 22.53 ms is achieved for the Sign Language Digits Database (SLDD). Further, an average prediction accuracy of 99.09% and average inference time of 13.28 ms is achieved for the Modified National Institute of Standards and Technology Database (MNIST). The model sizes implemented using TF-Lite are highly reduced to 1.53 MB for SLDD and 148 KB for the MNIST database. The obtained accuracy, inference time and model sizes are better than published results.
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    Neural Network-Based Architecture for Sentiment Analysis in Indian Languages
    (De Gruyter, 2018-06) Sharma, Yashvardhan
    Sentiment analysis refers to determining the polarity of the opinions represented by text. The paper proposes an approach to determine the sentiments of tweets in one of the Indian languages (Hindi, Bengali, and Tamil). Thirty-nine sequential models have been created using three different neural network layers [recurrent neural networks (RNNs), long short-term memory (LSTM), convolutional neural network (CNN)] with optimum parameter settings (to avoid over-fitting and error accumulation). These sequential models have been investigated for each of the three languages. The proposed sequential models are experimented to identify how the hidden layers affect the overall performance of the approach. A comparison has also been performed with existing approaches to find out if neural networks have an added advantage over traditional machine learning techniques.
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    OSVConTramer: A Hybrid CNN and Transformer based Online Signature Verification
    (IEEE, 2023) Gautam, Avinash
    The advances in Deep Learning (DL) resulted in the development Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNN) based Online Signature Verification (OSV) frameworks. The main drawback with LSTM based networks is the limited parallelization of model training. The CNN based frameworks are efficient in learning local feature dependencies, but fail to apprehend long term feature dependencies. The current works confirmed the success of Transformer based models in long term time series classification (LTTSC) problems due to efficient capturing of context-dependent global feature interactions. Hence, to achieve higher classification accuracy, in this work, we propose a first of its kind of an attempt, in which, we combine CNN and Transformer for Online Signature Verification, named OSVConTramer. The proposed OSVConTramer efficiently learns optimal local and global dependencies of an input signature feature vector and outperforms previous CNN and LSTM based OSV frameworks achieving state-of-the-art classification accuracy. On the widely used MCYT-100, SVC, and SUSIG datasets, specific to one shot learning, our model achieves a SOTA EER of 10.85%, 5.45%, and 6.32%, respectively. The results of the experimental analysis confirms that the accuracy outcomes of OSV frameworks is improved significantly by the optimal learning of the relationships between local and global feature dependency.
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    Deep Ear Biometrics for Gender Classification
    (Springer, 2023-07) Bera, Asish
    Human gender classification based on biometric features is a major concern for computer vision due to its vast variety of applications. The human ear is popular among researchers as a soft biometric trait, because it is less affected by age or changing circumstances and is non-intrusive. In this study, we have developed a deep convolutional neural network (CNN) model for automatic gender classification using the samples of ear images. The performance is evaluated using four cutting-edge pre-trained CNN models. In terms of trainable parameters, the proposed technique requires significantly less computational complexity. The proposed model has achieved 93% accuracy on the EarVN1.0 ear dataset.
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    PND-Net: plant nutrition deficiency and disease classification using graph convolutional network
    (Springer Nature, 2024-07) Bera, Asish
    Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. Hence, continuous health monitoring of plant is very crucial for handling plant stress. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. Furthermore, a GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), has been evaluated on two public datasets for nutrition deficiency, and two for disease classification using four backbone CNNs. The best classification performances of the proposed PND-Net are as follows: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40: 95.50%, and BreakHis 100: 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, the proposed method has been evaluated using five-fold cross validation and achieved improved performances on these datasets. Clearly, the proposed PND-Net effectively boosts the performances of automated health analysis of various plants in real and intricate field environments, implying PND-Net’s aptness for agricultural growth as well as human cancer classification.
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    A Deep Learning Approach for Molecular Crystallinity Prediction
    (Springer, 2019-05) Khungar, Bharti
    With the success of Convolutional Neural Networks (CNN) in computer vision domain, cheminformatics is slowly moving away from feature Engineering towards Network Engineering. New deep networks and approaches are being proposed to explore the chemical behavior and their properties. In this paper, we propose a deep learning approach using Convolutional Neural Network for predicting the crystallization propensity of an organic molecule. The work is inspired from Chemception and architecture is based on the Inception-Resnet v2 model. The proposed approach only requires a 2D molecular drawing to predict if the molecule has a good probability of forming crystals, without the need of any molecular descriptor, any advanced chemistry knowledge or any study of crystal growth mechanisms. We have evaluated our approach on the Cambridge Structural Database (CSD) and the ZINC datasets. Compared with the machine learning approach of generating molecular descriptors plus SVM classification, our proposed approach gives a better classification accuracy.
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    Convolutional Neural Network-Based Human Identification Using Outer Ear Images
    (Springer, 2018-10) Ajmera, Pawan K.; Sinha, Yash
    This paper presents a deep learning approach for ear localization and recognition. The comparable complexity between human outer ear and face in terms of its uniqueness and permanence has increased interest in the use of ear as a biometric. But similar to face recognition, it poses challenges such as illumination, contrast, rotation, scale, and pose variation. Most of the techniques used for ear biometric authentication are based on traditional image processing techniques or handcrafted ensemble features. Owing to extensive work in the field of computer vision using convolutional neural networks (CNNs) and histogram of oriented gradients (HOG), the feasibility of deep neural networks (DNNs) in the field of ear biometrics has been explored in this research paper. A framework for ear localization and recognition is proposed that aims to reduce the pipeline for a biometric recognition system. The proposed framework uses HOG with support vector machines (SVMs) for ear localization and CNN for ear recognition. CNNs combine feature extraction and ear recognition tasks into one network with an aim to resolve issues such as variations in illumination, contrast, rotation, scale, and pose. The feasibility of the proposed technique has been evaluated on USTB III database. This work demonstrates 97.9% average recognition accuracy using CNNs without any image preprocessing, which shows that the proposed approach is promising in the field of biometric recognition.
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    Area-optimal FPGA implementation of the YOLO v2 algorithm using High-Level Synthesis
    (IEEE, 2020) Asati, Abhijit; Shekhar, Chandra
    Field-programmable gate arrays (FPGAs) have been used as pre-silicon validation platforms in VLSI designs. In this paper, we propose a FPGA-based you-only-look-once (YOLO) v2 object detector implementation that provides better performance in terms of speed, achieves higher accuracy, and requires fewer resources compared with the alternatives. It is constructed using a convolutional deep neural network (CNN). We apply high-level synthesis (HLS) to model and optimize the implementation using multiple directives, such as pipelining, loop unrolling, in-lining, etc. The proposed YOLO v2 design is implemented on a Xilinx Zynq xc7z020clg484-1 device. We run simulations to test its functionality using an xSim simulator. The proposed implementation not only runs faster, but it utilizes an order of magnitude fewer resources than available implementations in the literature.
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    SR-GNN: Spatial Relation-Aware Graph Neural Network for Fine-Grained Image Categorization
    (IEEE, 2022-09) Bera, Asish
    Over the past few years, a significant progress has been made in deep convolutional neural networks (CNNs)-based image recognition. This is mainly due to the strong ability of such networks in mining discriminative object pose and parts information from texture and shape. This is often inappropriate for fine-grained visual classification (FGVC) since it exhibits high intra-class and low inter-class variances due to occlusions, deformation, illuminations, etc. Thus, an expressive feature representation describing global structural information is a key to characterize an object/scene. To this end, we propose a method that effectively captures subtle changes by aggregating context-aware features from most relevant image-regions and their importance in discriminating fine-grained categories avoiding the bounding-box and/or distinguishable part annotations. Our approach is inspired by the recent advancement in self-attention and graph neural networks (GNNs) approaches to include a simple yet effective relation-aware feature transformation and its refinement using a context-aware attention mechanism to boost the discriminability of the transformed feature in an end-to-end learning process. Our model is evaluated on eight benchmark datasets consisting of fine-grained objects and human-object interactions. It outperforms the state-of-the-art approaches by a significant margin in recognition accuracy.
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    Human Gender Classification Based on Hand Images Using Deep Learning
    (Springer, 2023-01) Bera, Asish
    Soft biometric traits (e.g., gender, age, etc. can characterize very relevant personal information. The hand-based traits are studied for traditional/hard biometric recognition for diverse applications. However, little attention is focused to tackle soft biometrics using hand images. In this paper, human gender classification is addressed using the frontal and dorsal hand images of a human. A new hand dataset is created at the Jadavpur University, India denoted as JU-HD for experiments. It represents significant posture variations in an uncontrolled laboratory environment. Sample hand images of 57 persons are collected to incorporate more user-flexibility in posing the hands that incur additional challenges to discriminate the person’s gender. Five backbone CNNs are used to develop a deep model for gender classification. The method achieves 90.49% accuracy on JU-HD using Inception-v3.