A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit

An Evidence-Based Spectral Clustering (EBSC) algorithm that works well for data with mixed numeric and nominal features is presented. A similarity measure based on evidence accumulation is adopted to define the similarity measure between pairs of objects, which makes no assumptions of the underlying distributions of the feature values. A spectral clustering algorithm is employed on the similarity matrix to extract a partition of the data. The performance of EBSC has been studied on real data sets. Results demonstrate the effectiveness of this algorithm in clustering mixed data tasks. Comparisons with other related clustering schemes illustrate the superior performance of this approach.

Clustering Mixed Data Based on Evidence Accumulation

The number of Internet of Things (IoT) devices to be connected via the Internet is overgrowing. The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments. Machine Learning (ML) plays a pivotal role in QoS enhancement, connectivity, and provisioning of smart applications. Therefore, this survey focuses on the use of ML for enhancing IoT applications. We also provide an in-depth overview of the variety of IoT applications that can be enhanced using ML, such as smart cities, smart homes, and smart healthcare. For each application, we introduce the advantages of using ML. Finally, we shed light on ML challenges for future IoT research, and we review the current literature based on existing works.

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Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS.

SPECT nuclear medicine imaging is widely used for treating, diagnosing, evaluating and preventing various serious diseases. The automated classification of medical images is becoming increasingly important in developing computer-aided diagnosis systems. Deep learning, particularly for the convolutional neural networks, has been widely applied to the classification of medical images. In order to reliably classify SPECT bone images for the automated diagnosis of metastasis on which the SPECT imaging solely focuses, in this paper, we present several deep classifiers based on the deep networks. Specifically, original SPECT images are cropped to extract the thoracic region, followed by a geometric transformation that contributes to augment the original data. We then construct deep classifiers based on the widely used deep networks including VGG, ResNet and DenseNet by fine-tuning their parameters and structures or self-defining new network structures. Experiments on a set of real-world SPECT bone images show that the proposed classifiers perform well in identifying bone metastasis with SPECT imaging. It achieves 0.9807, 0.9900, 0.9830, 0.9890, 0.9802 and 0.9933 for accuracy, precision, recall, specificity, F-1 score and AUC, respectively, on the test samples from the augmented dataset without normalization.

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Deep learning based automated diagnosis of bone metastases with SPECT thoracic bone images.

Pneumonia is an acute respiratory infection that has led to significant deaths of people worldwide. This lung disease is more common in people older than 65 and children under five years old. Although the treatment of pneumonia can be challenging, it can be prevented by early diagnosis using Computer-Aided Diagnosis (CAD) systems. Chest X-Rays (CXRs) are currently the primary imaging tool for detection of pneumonia, which are widely used by radiologists. While the standard approach of detecting pneumonia is based on clinicians’ decisions, various Deep Learning (DL) methods have been developed for detection of pneumonia considering CAD system. In this regard, a novel hybrid Convolutional Neural Network (CNN) model is proposed using three classification approaches. In the first classification approach, Fully-Connected (FC) layers are utilized for the classification of CXR images. This model is trained for several epochs and the weights that result in the highest classification accuracy are saved. In the second classification approach, the trained optimized weights are utilized to extract the most representative CXR image features and Machine Learning (ML) classifiers are employed to classify the images. In the third classification approach, an ensemble of the proposed classifiers is created to classify CXR images. The results suggest that the proposed ensemble classifier using Support Vector Machine (SVM) with Radial Basis Function (RBF) and Logistic Regression (LR) classifiers has the best performance with 98.55% accuracy. Ultimately, this model is deployed to create a web-based CAD system to assist radiologists in pneumonia detection with a significant accuracy.

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Pneumonia Detection Proposing a Hybrid Deep Convolutional Neural Network Based on Two Parallel Visual Geometry Group Architectures and Machine Learning Classifiers

Deep learning is an essential method of machine learning. Deep learning is rapidly suitable for the most sophisticated stage of a technology, prominent to enriched performance in numerous medical applications. The latest growth in machine learning, specifically with respect to deep learning, aids in recognition, classification, and computation of patterns in medical images. The main aim of these improvements is the ability to derive feature representation from learned data rather than designing those features by hand from domain-specific knowledge. In deep learning, the bottom-level network represents a low-level feature representation while the top-level network represents the output feature information. The computation of a deep learning network is faster with cheap hardware. In this chapter, we present the essentials of deep learning methods with convolutional neural networks and analyze their achievements in medical image analysis, such as in deep feature representation, detection, segmentation, classification, and prediction. Finally, we conclude by a discussion of research challenges and indicate future directions for further enhancements.

Medical Image Analysis With Deep Neural Networks

Machine learning algorithm for clustering of heart disease and chemoinformatics datasets

Clustering algorithm for mixed datasets using density peaks and Self-Organizing Generative Adversarial Networks

Deep learning methods play a vital role in Internet of things analytics. One of the main subgroups of machine learning algorithm is Deep Learning. Raw data is collected from devices. Collecting data from all situations and doing pre-processing is complex. Monitoring data through sensors continuously is also complex and expensive. Deep learning algorithms will solve these types of issues. A deep learning method signifies at various levels of representation from lower level features to very higher level features of data. The higher level features provide more abstract thoughts of information than the lower level which contains raw data. It is a developing methodology and has been commonly applied in art, image caption, machine translation, natural language processing, object detection, robotics, and visual tracking. The main purpose of using deep learning algorithms are such as faster processing, low-cost hardware, and modern growths in machine learning techniques. This review paper gives an understanding of deep learning methods and their recent advances in Internet of things.

K. Balaji

Papers

Medical Image Analysis With Deep Neural Networks

Machine learning algorithm for clustering of heart disease and chemoinformatics datasets

Clustering algorithm for mixed datasets using density peaks and Self-Organizing Generative Adversarial Networks

Recent Trends in Deep Learning with Applications

Machine learning algorithm for cluster analysis of mixed dataset based on instance-cluster closeness metric