Improving Inertial Sensor-Based Activity Recognition in Neurological Populations

Inertial sensor-based human activity recognition (HAR) has a range of healthcare applications as it can indicate the overall health status or functional capabilities of people with impaired mobility. Typically, artificial intelligence models achieve high recognition accuracies when trained with rich and diverse inertial datasets. However, obtaining such datasets may not be feasible in neurological populations due to, e.g., impaired patient mobility to perform many daily activities. This study proposes a novel framework to overcome the challenge of creating rich and diverse datasets for HAR in neurological populations. The framework produces images from numerical inertial time-series data (initial state) and then artificially augments the number of produced images (enhanced state) to achieve a larger dataset. Here, we used convolutional neural network (CNN) architectures by utilizing image input. In addition, CNN enables transfer learning which enables limited datasets to benefit from models that are trained with big data. Initially, two benchmarked public datasets were used to verify the framework. Afterward, the approach was tested in limited local datasets of healthy subjects (HS), Parkinson’s disease (PD) population, and stroke survivors (SS) to further investigate validity. The experimental results show that when data augmentation is applied, recognition accuracies have been increased in HS, SS, and PD by 25.6%, 21.4%, and 5.8%, respectively, compared to the no data augmentation state. In addition, data augmentation contributes to better detection of stair ascent and stair descent by 39.1% and 18.0%, respectively, in limited local datasets. Findings also suggest that CNN architectures that have a small number of deep layers can achieve high accuracy. The implication of this study has the potential to reduce the burden on participants and researchers where limited datasets are accrued

Editorial: Emerging applications of text analytics and natural language processing in healthcare

WOS:001033976100001Text analytics and natural language processing (NLP) have emerged as powerful tools in healthcare, revolutionizing patient care, clinical research, and public health administration. Over the years, as healthcare databases expand exponentially, healthcare providers, pharmaceutical and biotech industries are utilizing both tools to enhance patient outcome

Predicting occupational injury causal factors using text-based analytics : A systematic review

Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description of how the incidents occurred. Thus, the narrative is a useful information to extract, classify and analyze occupational injury. This study provides a systematic review of text mining and Natural Language Processing (NLP) applications to extract text narratives from occupational injury reports. A systematic search was conducted through multiple databases including Scopus, PubMed, and Science Direct. Only original studies that examined the application of machine and deep learning-based Natural Language Processing models for occupational injury analysis were incorporated in this study. A total of 27, out of 210 articles were reviewed in this study by adopting the Preferred Reporting Items for Systematic Review (PRISMA). This review highlighted that various machine and deep learning-based NLP models such as K-means, Naïve Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors were applied to predict occupational injury. On top of these models, deep neural networks are also included in classifying the type of accidents and identifying the causal factors. However, there is a paucity in using the deep learning models in extracting the occupational injury reports. This is due to these techniques are pretty much very recent and making inroads into decision-making in occupational safety and health as a whole. Despite that, this paper believed that there is a huge and promising potential to explore the application of NLP and text-based analytics in this occupational injury research field. Therefore, the improvement of data balancing techniques and the development of an automated decision-making support system for occupational injury by applying the deep learning-based NLP models are the recommendations given for future research

Exploring Post-Quantum Cryptographic Schemes for TLS in 5G NB-IOT: Feasibility and Recommendations

Narrowband Internet of Things (NB-IoT) is a wireless communication technology that enables a wide range of applications, from smart cities to industrial automation. As a part of the 5G extension, NB-IoT promises to connect billions of devices with low-power and low-cost requirements. However, with the advent of quantum computers, the incoming NB-IoT era is already under threat due to conventional cryptographic algorithms that might be adapted to secure devices in NB-IoT being susceptible to be broken soon. In this context, we investigate the feasibility of using post-quantum key exchange and signature algorithms for securing NB-IoT applications. We develop a realistic ns-3 environment to represent the characteristics of NB-IoT networks and analyze the usage of post-quantum algorithms to secure communication. In this context, we investigate the feasibility of using post-quantum key exchange and signature algorithms for securing NB-IoT applications

Exploring Post-Quantum Cryptographic Schemes for TLS in 5G Nb-IoT: Feasibility and Recommendations

Narrowband Internet of Things (NB-IoT) is a wireless communication technology that enables a wide range of applications, from smart cities to industrial automation. As a part of the 5G extension, NB-IoT promises to connect billions of devices with low-power and low-cost requirements. However, with the advent of quantum computers, the incoming NB-IoT era is already under threat by these devices, which might break the conventional cryptographic algorithms that can be adapted to secure NB-IoT devices on large scale. In this context, we investigate the feasibility of using post-quantum key exchange and signature algorithms for securing NB-IoT applications. We develop a realistic ns-3 environment to represent the characteristics of NB-IoT networks and analyze the usage of post-quantum algorithms to secure communication. Our findings suggest that using NIST-selected post-quantum key-exchange protocol Kyber does not introduce significant overhead, but post-quantum signature schemes can result in impractical latency times and lower throughput

A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods

Among the leading causes of mortality and morbidity in people are lung and colon cancers. They may develop concurrently in organs and negatively impact human life. If cancer is not diagnosed in its early stages, there is a great likelihood that it will spread to the two organs. The histopathological detection of such malignancies is one of the most crucial components of effective treatment. Although the process is lengthy and complex, deep learning (DL) techniques have made it feasible to complete it more quickly and accurately, enabling researchers to study a lot more patients in a short time period and for a lot less cost. Earlier studies relied on DL models that require great computational ability and resources. Most of them depended on individual DL models to extract features of high dimension or to perform diagnoses. However, in this study, a framework based on multiple lightweight DL models is proposed for the early detection of lung and colon cancers. The framework utilizes several transformation methods that perform feature reduction and provide a better representation of the data. In this context, histopathology scans are fed into the ShuffleNet, MobileNet, and SqueezeNet models. The number of deep features acquired from these models is subsequently reduced using principal component analysis (PCA) and fast Walsh–Hadamard transform (FHWT) techniques. Following that, discrete wavelet transform (DWT) is used to fuse the FWHT’s reduced features obtained from the three DL models. Additionally, the three DL models’ PCA features are concatenated. Finally, the diminished features as a result of PCA and FHWT-DWT reduction and fusion processes are fed to four distinct machine learning algorithms, reaching the highest accuracy of 99.6%. The results obtained using the proposed framework based on lightweight DL models show that it can distinguish lung and colon cancer variants with a lower number of features and less computational complexity compared to existing methods. They also prove that utilizing transformation methods to reduce features can offer a superior interpretation of the data, thus improving the diagnosis procedure

The Use of Digital Color Imaging and Machine Learning for the Evaluation of the Effects of Shade Drying and Open-Air Sun Drying on Mint Leaf Quality

The objective of this study was to reveal the usefulness of image processing and machine learning for the non-destructive evaluation of the changes in mint leaves caused by two natural drying techniques. The effects of shade drying and open-air sun drying on the ventral side (upper surface) and dorsal side (lower surface) of leaves were compared. Texture parameters were extracted from the digital color images converted to color channels R, G, B, L, a, b, X, Y, and Z. Models based on image features selected for individual color channels were built for distinguishing mint leaves in terms of drying techniques and leaf side using machine learning algorithms from groups of Lazy, Rules, and Trees. In the case of classification of the images of the ventral side of fresh and shade-dried mint leaves, an average accuracy of 100% and values of Precision, Recall, F-Measure, and MCC of 1.000 were obtained for color channels B (KStar and J48 machine learning algorithms), a (KStar and J48), b (KStar), and Y (KStar). The effect of open-air sun drying was greater. Images of the ventral side of fresh and open-air sun-dried mint leaves were completely correctly distinguished (100% correctness) for more color channels and algorithms, such as color channels R and G (J48), B, a and b (KStar, JRip, and J48), and X and Y (KStar). The classification of the images of the dorsal side of fresh and shade-dried mint leaves provided 100% accuracy in the case of color channel B (KStar) and a (KStar, JRip, and J48). The fresh and open-air sun-dried mint leaves imaged on the dorsal side were correctly classified at an accuracy of 100% for selected textures from color channels a (KStar, JRip, J48), b (J48), and Z (J48). The developed approach may be used in practice to monitor the changes in the structure of mint leaves caused by drying in a non-destructive, objective, cost-effective, and fast manner without the need to damage the leaves

Distinguishing Pickled and Fresh Cucumber Slices Using Digital Image Processing and Machine Learning

In the case of cucumber, postharvest challenges may focus on preserving the high quality and extending the shelf-life of the fruit. Digital image analysis provides objective information about the quality of food products and the changes in their properties as a result of postharvest processing. This study aimed to develop discriminative models for distinguishing the pickled and fresh cucumbers based on the texture parameters of slice images. The textures were extracted from slice images that were converted to individual color channels, L, a, b, R, G, B, X, Y, and Z. The obtained results prove the effects of the preservation on the image features of the cucumber flesh. Including selected textures in the discriminative models allowed for the complete differentiation of the preserved and fresh samples. The application of digital image processing enabled the evaluation of changes in the flesh of cucumber subjected to postharvest preservation

Integrated transcriptomic meta-analysis and comparative artificial intelligence models in maize under biotic stress

Abstract Biotic stress imposed by pathogens, including fungal, bacterial, and viral, can cause heavy damage leading to yield reduction in maize. Therefore, the identification of resistant genes paves the way to the development of disease-resistant cultivars and is essential for reliable production in maize. Identifying different gene expression patterns can deepen our perception of maize resistance to disease. This study includes machine learning and deep learning-based application for classifying genes expressed under normal and biotic stress in maize. Machine learning algorithms used are Naive Bayes (NB), K-Nearest Neighbor (KNN), Ensemble, Support Vector Machine (SVM), and Decision Tree (DT). A Bidirectional Long Short Term Memory (BiLSTM) based network with Recurrent Neural Network (RNN) architecture is proposed for gene classification with deep learning. To increase the performance of these algorithms, feature selection is made from the raw gene features through the Relief feature selection algorithm. The obtained finding indicated the efficacy of BiLSTM over other machine learning algorithms. Some top genes ((S)-beta-macrocarpene synthase, zealexin A1 synthase, polyphenol oxidase I, chloroplastic, pathogenesis-related protein 10, CHY1, chitinase chem 5, barwin, and uncharacterized LOC100273479 were proved to be differentially upregulated under biotic stress condition

Differentiation of Yeast-Inoculated and Uninoculated Tomatoes Using Fluorescence Spectroscopy Combined with Machine Learning

Artificial-intelligence-based analysis methods can provide objective and accurate results. This study aimed to evaluate the performance of machine learning algorithms to classify yeast-inoculated and uninoculated tomato samples using fluorescent spectroscopic data. For this purpose, three different tomato types were used: ‘local dwarf’, ‘Picador’, and ‘Ideal’. Discrimination analysis was applied with six different machine learning (ML) algorithms. Confusion matrices, average accuracies, F-Measure, Precision, ROC (receiver operating characteristic) Area, MCC (Matthews Correlation Coefficient), and precision-recall area values obtained as a result of the application of different ML algorithms were compared. Based on the fluorescence spectroscopic data, the application of six ML algorithms showed that the first two tomato types were classified with 100% accuracy and the last type was classified with 95% accuracy. The results of the study show that the fluorescence spectroscopy data are strongly representative of tomato species. ML methods fed with these data provide high-performance discrimination