Bengali text document categorization based on very deep convolution neural network

In pattern recognition, identifying Kannada handwritten numerals are a complex knot. This paper portrays an avenue that pikes us to attain a most potent Kannada Numerals recognition process. In this, handwritten Kannada characters are captivated in document fashion and are subjected to Pre-processing and attribute extraction processes. Pre-processing entails steps like noise removal, binarization, normalization, skew amendment, and thinning. Features are extricated by exploiting strategies like Drift Length Count, Direction related progression code, DWT and Curvelet Transfiguration Wrapping. For an impressive classification process deep convolution neural network classifier is preferred. Isolation accuracy of Kannada numeral aimed here will outsource 96% of accuracy.

Identification of crops is an important topic in the agricultural domain. Hyperspectral remote sensing data are very useful for crop feature extraction and classification. Remote sensing data is an unstructured data and Convolutional Neural Network (CNN) can work on unstructured data efficiently. This paper presents an evaluation of CNN for crop classification using the Indian Pines standard dataset obtained from the AVIRIS sensor and the study area dataset obtained from the EO-1hyperion sensor. Optimized CNN has been tuned by training the model on different parameters. It has been compared with two classification algorithms: Deep Neural Network (DNN) and Convolutional Autoencoder. According to the test results, the proposed optimized CNN model provided better results as compared to the other two methods. CNN has given 97 ± 0.58% overall accuracy for the Indian Pines standard dataset and 78 ± 2.43% for our study area dataset.

Identification of retinal diseases is a test for the ophthalmologists as the anomalies are just unmistakable at the beginning period. Early detection of these diseases can avoid lasting vision misfortune. Dealing with a lot of retinal images and location of variations from the norm because of these infections is difficult just as tedious. In this work, the deep learning algorithm has proposed to check the abnormality condition of retina with the help of retinal fundus images. In deep leaning a training set is produced with features of variations from the norm present in the retinal images and the infection the retina is experiencing. The deep Convolutional Neural Network (CNN) classifier predicts the infection for every retinal images in the wake of social event the learning from training the set. The rightness of desire is resolved to evaluate the viability of the classifier. The proposed technique was executed in MATLAB and assessed both normal and abnormal diabetic retinopathy retinal images of IDRID, ROC, and local datasets. The proposed technique has gotten better execution measurements, for example, sensitivity of 98.2%, Specificity of 98.45%, accuracy of 98.56% and average area under receiver operating characteristics of 0.9 when contrasted with different conditions of the workmanship strategies.

The report presents an algorithm for constructing a convolutional hierarchical neural network classifier, which is a modification of the algorithm for constructing hierarchical neural network classifiers suggested before. The original algorithm was designed to exploit intrinsic class hierarchy to build a class tree with a neural network in each node classifying groups of initial classes (in a non-terminal node) or a subset of original classes (in a terminal node). The convolutional modification utilizes convolutional neural networks instead of regular fully connected networks in order to apply the model to image classification tasks. Use of class hierarchy for image classification should reduce the number of adjusted neural network parameters compared to deep convolutional neural networks, and therefore it should reduce training and inference time. In this context the algorithm may be compared with some pruning techniques. The convolutional hierarchical neural network classifier inherits some hyperparameters of a conventional hierarchical neural network classifier, like the activation threshold and the threshold by the share of voting patterns. The goal of this study was to explore different strategies of choosing these hyperparameters. To test these strategies, we used the CIFAR-10 dataset. Also, for demonstration purposes we apply the convolutional hierarchical neural network classifier to the CIFAR-100 dataset.

In this manuscript, the combination of IoT and Multilayer Hybrid Dropout Deep-learning Model for waste image categorization is proposed to categorize the wastes as bio waste and non-bio waste. The input captured images are pre-processed and remove noises in the captured images. Under this approach, a Nature inspired Multilayer Hybrid Dropout Deep-learning Model is proposed. Multilayer Hybrid Dropout Deep-learning Model is the consolidation of deep convolutional neural network and Dropout Extreme Learning Machine classifier. Here, deep convolutional neural network is used for feature extraction and Dropout Extreme Learning Machine classifier for categorizing the waste images. To improve the classification accurateness, Horse herd optimization algorithm is used to optimize the parameter of the Dropout Extreme Learning Machine classifier. The objective function is to maximize the accuracy by minimize the computational complexity. The simulation is executed in MATLAB. The proposed Multilayer Hybrid Dropout Deep-learning Model and Horse herd optimization algorithm attains higher accuracy 39.56% and 42.46%, higher Precision 48.74% and 34.56%, higher F-Score 32.5% and 45.34%, higher Sensitivity 24.45% and 34.23%, higher Specificity 31.43% and 21.45%, lower execution time 0.019(s) and 0.014(s) compared with existing waste management and classification using convolutional neural network with hyper parameter of random search optimization algorithm waste management and classification using clustering approach with Ant colony optimization algorithm. Finally, the proposed method categorizes the waste image accurately.

Thyroid cancer has a high prevalence all over the world. Accurate thyroid nodule diagnosis can lead to effective treatment and decrease the mortality rate. Ultrasound imaging is a safe, portable, and inexpensive tool for thyroid nodule monitoring. However, the widespread use of ultrasound has also resulted in over-diagnosis and over-treatment of nodules. There is also large variability in the assessment and characterization of nodules. Thyroid nodule classification requires precise delineation of the nodule boundary which is tedious and time- consuming. Automatic segmentation of nodule boundaries is highly desirable, however, it is challenging due to the wide range of nodule appearances, shapes, and sizes. In this study, we propose an end-to-end pipeline for nodule segmentation and classification. A residual dilated UNet (resDUnet) model is proposed for nodule segmentation. The output of resDUnet is fed to two rule-based classifiers to categorize the composition and echogenicity of the segmented nodule. We evaluate our segmentation method on a large dataset of 352 ultrasound images reviewed by a certified radiologist. When compared with ground-truth, resDUnet gives a higher Dice score than the standard UNet (82% vs. 81%). Our method requires minimal user interaction and it is robust to reasonable variations in the user-specified region-of-interest. We expect the proposed method to reduce variability in thyroid nodule assessment which results in more efficient and cost-effective monitoring of thyroid cancer.

The early detection of dementia is crucial in independent life style of elderly people. Main intention of this study is to propose device-free non-privacy invasive Deep Convolutional Neural Network classifier (DCNN) for Martino-Saltzman's (MS) travel patterns of elderly people living alone using open dataset collected by binary (passive infrared) sensors. Travel patterns are classified as direct, pacing, lapping, or random according to MS model. MS travel pattern is highly related with person's cognitive state, thus can be used to detect early stage of dementia. The dataset was collected by monitoring a cognitively normal elderly resident by wireless passive infrared sensors for 21 months. First, over 70000 travel episodes are extracted from the dataset and classified by MS travel pattern classifier algorithm for the ground truth. Later, 12000 episodes (3000 for each pattern) were randomly selected from the total episodes to compose training and testing dataset. Finally, DCNN performance was compared with three other classical machine-learning classifiers. The Random Forest and DCNN yielded the best classification accuracies of 94.48% and 97.84%, respectively. Thus, the proposed DCNN classifier can be used to infer dementia through travel pattern matching.

In this work we present an analysis of the region of interest (ROI) generation from the source thermal night vision images through the double thresholding segmentation technique as a part of pedestrian detection procedure. In some cases, pedestrians do not fit into the generated ROIs. To solve the problem we propose to adjust (slightly enlarge) the segmented ROI. Through this, it is possible to reduce miss rate for the aggregated channel feature (ACF) classifier from 29.1% to 24.8% and for the deep convolutional neural network (CNN) classifier from 24.0% to 22.4%, with negligible impact on the processing time.

This paper demonstrates exceptional performance of approximately 10.0 dB learning-based gain using the Deep Convolutional Neural Network (DCNN) for demodulation of a Rayleigh-faded wireless data signal. We simulate FSK demodulation over an AWGN Rayleigh fading channel with average signal to noise ratios (SNR) from 10 dB to 20 dB. The most recent and accurate classifier is the Deep Convolutional Neural Network (DCNN) which resulted in the lowest error bit probabilities between 0.00128 to 0.00019 for the range of SNRs. A comparative study has been applied between DCNN and other machine learning classifiers such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Multi-Layer Perceptron (MLP) which give bit error probabilities between 0.021 to 0.002, and Quadratic Discriminant Analysis (QDA) which gives bit error probabilities between 0.027 to 0.003. Frequency-shift keying (FSK) demodulation using matched filtering showed bit error probabilities between 0.025 to 0.0025. We also discuss the complexity issues with the DCNN regarding decoding rates and training set sizes. This work shows how much the DCNN would provide substantial benefit as the demodulator.

Self-mixing interferometry (SMI) is superior to other laser interferometry methods due to its simplicity and compactness. However, SMI signals are often complex and difficult to process due to interference in the form of variations in the effective reflectivity of the target, noisy signals, complex signal shapes, and other dependencies. Deep neural networks have been a very popular area of research in computer artificial intelligence in recent years, allowing more implicit features to be uncovered than traditional shallow machine learning. It has been shown that convolutional and back-propagation neural networks can be used for SMI signal processing. There are also studies that have used machine learning genetic algorithms for absolute distance measurement. Based on the above, this study used convolutional neural networks to form a deep neural network for absolute distance measurement based on SMI technology. We first trained the deep convolutional neural network at different feedback strengths. The results of the Convolutional Neural Network (CNN) model showed a coefficient of determination of 0.9987. which is consistent with the required model. The trained network was then used to estimate absolute distances with and without the addition of noise. The comparison proves that the proposed method is noise-proof and has high adaptability for measurements under different conditions.

Lung cancer patients’ chances of survival can increase with early detection. For the diagnosis of lung cancer, precise lung nodule identification in computed tomography (CT) images is crucial. The variety of the lung nodules and the complexity of the surroundings, however, have made robust nodule identification a challenging problem. To address this challenge, the Extensible Hunt Optimization-based deep Convolutional Neural Network classifier (EHO-deep CNN), is developed in this research for detecting the lung nodules present in the CT images. The proposed model in which the preprocessing is performed through the median filter to eliminate the artifacts in the image. Specifically, the images are segmented by adopting the centralized superpixels segmentation-based iterative clustering (CSSBIC) technique that enhances the accuracy of segmentation. The proposed approach relies on the GLCM and the VGG16-based feature extraction to explicate the discrimination of the lung nodule features. In addition, the retrieved features are combined and fed forwarded to the Deep CNN classifier that is optimally tuned via the EHO algorithm. The EHO algorithm is developed by fusing the fitness function of Border collie dogs with the hunting characteristics of Dingo to enhance convergence rate and performance. The proposed EHO-deep CNN is reported in terms of accuracy, sensitivity, and specificity values as 98.13%, 98.98%, and 97.28% when measuring TP 80 for the LIDC-IDRI dataset and 96.09%, 96.70%, and 95.48% for measuring k-fold 10. Further, implementing the suggested model obtains the values of 95.34%, 92.88%, and 97.80% for the LUNA 16 dataset when measuring TP 80 and 98.21%, 98.06%, 98.35% when measuring k-fold 10, which outperforms other existing techniques in effectiveness.

Due to the advent of modern embedded systems and mobile devices with constrained resources, there is a great demand for incredibly efficient deep neural networks for machine learning purposes. There is also a growing concern of privacy and confidentiality of user data within the general public when their data is processed and stored in an external server which has further fueled the need for developing such efficient neural networks for real-time inference on local embedded systems. The scope of our work presented in this paper is limited to image classification using a convolutional neural network. A Convolutional Neural Network (CNN) is a class of Deep Neural Network (DNN) widely used in the analysis of visual images captured by an image sensor, designed to extract information and convert it into meaningful representations for real-time inference of the input data. In this paper, we propose a neoteric variant of deep convolutional neural network architecture to ameliorate the performance of existing CNN architectures for real-time inference on embedded systems. We show that this architecture, dubbed CondenseNeXt, is remarkably efficient in comparison to the baseline neural network architecture, CondenseNet, by reducing trainable parameters and FLOPs required to train the network whilst maintaining a balance between the trained model size of less than 3.0 MB and accuracy trade-off resulting in an unprecedented computational efficiency.

Resnet-based modified red deer optimization with DLCNN classifier for plant disease identification and classification

Methodologies to assess stem cell differentiation in the culturing state are needed for regenerative medicine and tissue engineering techniques. In recent years, convolutional neural networks (CNNs), a class of deep neural networks, have made impressive advancements in image-based classification, recognition and detection tasks. CNNs have been introduced as a non-invasive cell characterization method by learning features directly from image data of unlabeled cells. Furthermore, this approach serves as a rapid and inexpensive methodology with high performance compared to traditional techniques that require complex laboratory procedures including antibody staining and gene expression analysis. Here, we studied the potential of the CNNs approach to recognize stem cell differentiation based on cell morphology utilizing phasecontrast microscopy images.We have examined the differentiation potential of bone marrow mesenchymal stem cells (BMSCs) into tenocytes, with the treatment of bone morphogenetic protein-12 (BMP-12). After treatment, the phase-contrast images of cells were obtained directly from cell culture flasks to train CNN and the differentiated phenotype of stem cells was characterized by immunostaining. CNN was able to classify the cells into three groups including non-stem cells (chondrocytes), stem cells (BMSCs) and differentiated stem cells (tenocytes) based on their morphology with 92.2 % accuracy. The presented study revealed that CNN performed faster and non-invasive cell classification task compared to traditional methodologies.

Convolutional neural network (CNN) and recurrent neural network (RNN) are two popular architectures used in text classification. Traditional methods to combine the strengths of the two networks rely on streamlining them or concatenating features extracted from them. In this paper, we propose a novel method to keep the strengths of the two networks to a great extent. In the proposed model, a convolutional neural network is applied to learn a 2D weight matrix where each row reflects the importance of each word from different aspects. Meanwhile, we use a bidirectional RNN to process each word and employ a neural tensor layer that fuses forward and backward hidden states to get word representations. In the end, the weight matrix and word representations are combined to obtain the representation in a 2D matrix form for the text. We carry out experiments on a number of datasets for text classification. The experimental results confirm the effectiveness of the proposed method.