A Prototype-Based Modified DBSCAN for Gene Clustering

AbstractIn this paper, we propose, a novel DBSCAN method to cluster the gene expression data. The main problem of DBSCAN is its quadratic computational complexity. We resolve this drawback by using the prototypes produced from a squared error clustering method such as K-means. Then, the DBSCAN technique is applied efficiently using these prototypes. In our algorithm, during the iterations of DBSCAN, if a point from an uncovered prototype is assigned to a cluster, then all the other points of such prototype belongs to the same cluster. We have carried out excessive experiments on various two dimensional artificial and multi-dimensional biological data. The proposed technique is compared with few existing techniques. It is observed that proposed algorithm outperforms the existing methods

Synthesis and characterization of pure and Cu-doped WO3 thin films for high performance of toxic gas sensing applications

Oxygen vacancies and the high surface area of nanostructured materials offers stronger gas adsorption active sites for improved gas sensing performance. In the present work, we have deposited WO3-based thin films using a chemical spray pyrolysis technique at a substrate temperature of 400 °C with other optimized preparation parameters. Besides, this study reports the influence of Cu doping at various weight percentages on the structural, morphological, optical and toxic gas sensing properties of WO3 films in detail. The crystalline structure of the material was investigated using the X-ray diffraction technique. Each X-ray diffraction analysis of the films demonstrated a polycrystalline nature, matching the hexagonal phase of WO3 with the (200) plane of preferential orientation of crystal growth. The optical characteristics of the thin film's material were analyzed by UV–Vis absorption spectroscopy. The optical energy bandgap (Eg) of the deposited thin films was estimated using the Tauc relation, and the value was decreased along with the increase in the Cu-doping concentration. The surface roughness of the films has been investigated using atomic force microscopy. The TEM patterns of sprayed Cu-doped WO3 thin films exhibit nanocrystalline features. Detecting toxic gasses is of great importance across the globe due to an alarming enhancement in respiratory, ocular, skin, and lung diseases. Hence, cost-effective and room temperature-operated Cu-doped WO3 thin film sensors to trace low concentrations of toxic gasses have been investigated and systematically reported. It is observed that the sensor response is to be increased with the increase in Cu-doping concentration, particularly the 4 wt.% Cu-doped sensor exhibited a response value of 7.2 and fast response and recovery times in the presence of 50 ppm ammonia vapor

Automatic disease diagnosis using optimised weightless neural networks for low-power wearable devices

Low-power wearable devices for disease diagnosis are used at anytime and anywhere. These are non-invasive and pain-free for the better quality of life. However, these devices are resource constrained in terms of memory and processing capability. Memory constraint allows these devices to store a limited number of patterns and processing constraint provides delayed response. It is a challenging task to design a robust classification system under above constraints with high accuracy. In this Letter, to resolve this problem, a novel architecture for weightless neural networks (WNNs) has been proposed. It uses variable sized random access memories to optimise the memory usage and a modified binary TRIE data structure for reducing the test time. In addition, a bio-inspired-based genetic algorithm has been employed to improve the accuracy. The proposed architecture is experimented on various disease datasets using its software and hardware realisations. The experimental results prove that the proposed architecture achieves better performance in terms of accuracy, memory saving and test time as compared to standard WNNs. It also outperforms in terms of accuracy as compared to conventional neural network-based classifiers. The proposed architecture is a powerful part of most of the low-power wearable devices for the solution of memory, accuracy and time issues

Blockchain based efficient tamper-proof EHR storage for decentralized cloud-assisted storage

In recent years, the rapid and wide-ranging implementation of a cloud-based electronic healthcare record (EHR) storage system has shown significant advantages in effectively managing EHR for healthcare organizations and patients. However, in the cloud-based EHR storage model, the patients no longer have direct control of their EHR, whereas healthcare organizations may access the outsourced EHR whenever necessary. It may always cause severe security issues, specifically when healthcare organizations collude with the cloud service provider (CSP) to conceal any medical malpractice. Therefore, to deal with these significant concerns, we have introduced a novel blockchain based efficient tamper-proof model for EHR storage in decentralized InterPlanetary File System (IPFS) storage in the cloud - “TAC-EHR”. The key idea of the model is that every operation involves outsourcing EHRs and integrating these EHRs into a transaction on the public blockchain provides computationally unforgeability to the outsourced EHRs. Moreover, the proposed EHR storage model can also manage batch outsourcing, i.e., numerous EHR outsourcing for multiple patients by multiple doctors simultaneously, in an effective manner. The experimental and security analysis demonstrates that the proposed blockchain based cloud-assisted EHR storage model efficiently assures intractability computationally and outperforms the existing models in terms of computational and communication overhead

Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm

Background and Objective Fatty Liver Disease (FLD) - a disease caused by deposition of fat in liver cells, is predecessor to terminal diseases such as liver cancer. The machine learning (ML) techniques applied for FLD detection and risk stratification using ultrasound (US) have limitations in computing tissue characterization features, thereby limiting the accuracy. Methods Under the class of Symtosis for FLD detection and risk stratification, this study presents a Deep Learning (DL)-based paradigm that computes nearly seven million weights per image when passed through a 22 layered neural network during the cross-validation (training and testing) paradigm. The DL architecture consists of cascaded layers of operations such as: convolution, pooling, rectified linear unit, dropout and a special block called inception model that provides speed and efficiency. All data analysis is performed in optimized tissue region, obtained by removing background information. We benchmark the DL system against the conventional ML protocols: support vector machine (SVM) and extreme learning machine (ELM). Results The liver US data consists of 63 patients (27 normal/36 abnormal). Using the K10 cross-validation protocol (90% training and 10% testing), the detection and risk stratification accuracies are: 82%, 92% and 100% for SVM, ELM and DL systems, respectively. The corresponding area under the curve is: 0.79, 0.92 and 1.0, respectively. We further validate our DL system using two class biometric facial data that yields an accuracy of 99%. Conclusion DL system shows a superior performance for liver detection and risk stratification compared to conventional machine learning systems: SVM and ELM

Extreme learning machine framework for risk stratification of fatty liver disease using ultrasound tissue characterization

Fatty Liver Disease (FLD) is caused by the deposition of fat in liver cells and leads to deadly diseases such as liver cancer. Several FLD detection and characterization systems using machine learning (ML) based on Support Vector Machines (SVM) have been applied. These ML systems utilize large number of ultrasonic grayscale features, pooling strategy for selecting the best features and several combinations of training/testing. As result, they are computationally intensive, slow and do not guarantee high performance due to mismatch between grayscale features and classifier type. This study proposes a reliable and fast Extreme Learning Machine (ELM)-based tissue characterization system (a class of Symtosis) for risk stratification of ultrasound liver images. ELM is used to train single layer feed forward neural network (SLFFNN). The input-to-hidden layer weights are randomly generated reducing computational cost. The only weights to be trained are hidden-to-output layer which is done in a single pass (without any iteration) making ELM faster than conventional ML methods. Adapting four types of K-fold cross-validation (K = 2, 3, 5 and 10) protocols on three kinds of data sizes: S0-original, S4-four splits, S8-sixty four splits (a total of 12 cases) and 46 types of grayscale features, we stratify the FLD US images using ELM and benchmark against SVM. Using the US liver database of 63 patients (27 normal/36 abnormal), our results demonstrate superior performance of ELM compared to SVM, for all cross-validation protocols (K2, K3, K5 and K10) and all types of US data sets (S0, S4, and S8) in terms of sensitivity, specificity, accuracy and area under the curve (AUC). Using the K10 cross-validation protocol on S8 data set, ELM showed an accuracy of 96.75% compared to 89.01% for SVM, and correspondingly, the AUC: 0.97 and 0.91, respectively. Further experiments also showed the mean reliability of 99% for ELM classifier, along with the mean speed improvement of 40% using ELM against SVM. We validated the symtosis system using two class biometric facial public data demonstrating an accuracy of 100%