Nishchal K. Verma

Arun K. Sharma, Nishchal K. Verma

Biomedical image classification requires capturing of bio-informatics based on specific feature distribution. In most of such applications, there are mainly challenges due to limited availability of samples for diseased cases and imbalanced nature of dataset. This article presents the novel framework of multi-head self-attention for vision transformer (ViT) which makes capable of capturing the specific image features for classification and analysis. The proposed method uses the concept of residual connection for accumulating the best attention output in each block of multi-head attention. The proposed framework has been evaluated on two small datasets: (i) blood cell classification dataset and (ii) brain tumor detection using brain MRI images. The results show the significant improvement over traditional ViT and other convolution based state-of-the-art classification models.

Adarsh Gupta, Japleen Kaur, Tanvi Doshi et al.

Knee Osteoarthritis (KOA) is a musculoskeletal condition that can cause significant limitations and impairments in daily activities, especially among older individuals. To evaluate the severity of KOA, typically, X-ray images of the affected knee are analyzed, and a grade is assigned based on the Kellgren-Lawrence (KL) grading system, which classifies KOA severity into five levels, ranging from 0 to 4. This approach requires a high level of expertise and time and is susceptible to subjective interpretation, thereby introducing potential diagnostic inaccuracies. To address this problem a stacked ensemble model of fine-tuned Convolutional Neural Networks (CNNs) was developed for two classification tasks: a binary classifier for detecting the presence of KOA, and a multiclass classifier for precise grading across the KL spectrum. The proposed stacked ensemble model consists of a diverse set of pre-trained architectures, including MobileNetV2, You Only Look Once (YOLOv8), and DenseNet201 as base learners and Categorical Boosting (CatBoost) as the meta-learner. This proposed model had a balanced test accuracy of 73% in multiclass classification and 87.5% in binary classification, which is higher than previous works in extant literature.

Seetaram Maurya, Nishchal K. Verma

Various methods for designing input features have been proposed for fault recognition in rotating machines using one-dimensional raw sensor data. The available methods are complex, rely on empirical approaches, and may differ depending on the condition monitoring data used. Therefore, this article proposes a novel algorithm to design input features that unifies the feature extraction process for different time-series sensor data. This new insight for designing/extracting input features is obtained through the lens of histogram theory. The proposed algorithm extracts discriminative input features, which are suitable for a simple classifier to deep neural network-based classifiers. The designed input features are given as input to the classifier with end-to-end training in a single framework for machine conditions recognition. The proposed scheme has been validated through three real-time datasets: a) acoustic dataset, b) CWRU vibration dataset, and c) IMS vibration dataset. The real-time results and comparative study show the effectiveness of the proposed scheme for the prediction of the machine's health states.

Arun K. Sharma, Nishchal K. Verma

The diagnostic performance of most of the deep learning models is greatly affected by the selection of model architecture and hyperparameters. Manual selection of model architecture is not feasible as training and evaluating the different architectures of deep learning models is a time-consuming process. Therefore, we have proposed a novel framework of evolutionary deep neural network which uses policy gradient to guide the evolution of DNN architecture towards maximum diagnostic accuracy. We have formulated a policy gradient-based controller which generates an action to sample the new model architecture at every generation such that the optimality is obtained quickly. The fitness of the best model obtained is used as a reward to update the policy parameters. Also, the best model obtained is transferred to the next generation for quick model evaluation in the NSGA-II evolutionary framework. Thus, the algorithm gets the benefits of fast non-dominated sorting as well as quick model evaluation. The effectiveness of the proposed framework has been validated on three datasets: the Air Compressor dataset, Case Western Reserve University dataset, and Paderborn university dataset.

Arun K. Sharma, Nishchal K. Verma

A faster response with commendable accuracy in intelligent systems is essential for the reliability and smooth operations of industrial machines. Two main challenges affect the design of such intelligent systems: (i) the selection of a suitable model and (ii) domain adaptation if there is a continuous change in operating conditions. Therefore, we propose an evolutionary Net2Net transformation (EvoN2N) that finds the best suitable DNN architecture with limited availability of labeled data samples. Net2Net transformation-based quick learning algorithm has been used in the evolutionary framework of Non-dominated sorting genetic algorithm II to obtain the best DNN architecture. Net2Net transformation-based quick learning algorithm uses the concept of knowledge transfer from one generation to the next for faster fitness evaluation. The proposed framework can obtain the best model for intelligent fault diagnosis without a long and time-consuming search process. The proposed framework has been validated on the Case Western Reserve University dataset, the Paderborn University dataset, and the gearbox fault detection dataset under different operating conditions. The best models obtained are capable of demonstrating an excellent diagnostic performance and classification accuracy of almost up to 100% for most of the operating conditions.

Mayank Pandey, Rachit Agarwal, Sandeep K. Shukla et al.

The advancement in the healthcare sector is entering into a new era in the form of Health 4.0. The integration of innovative technologies like Cyber-Physical Systems (CPS), Big Data, Cloud Computing, Machine Learning, and Blockchain with Healthcare services has led to improved performance and efficiency through data-based learning and interconnection of systems. On the other hand, it has also increased complexities and has brought its own share of vulnerabilities due to the heavy influx, sharing, and storage of healthcare data. The protection of the same from cyber-attacks along with privacy preservation through authenticated access is one of the significant challenges for the healthcare sector. For this purpose, the use of blockchain-based networks can lead to a considerable reduction in the vulnerabilities of the healthcare systems and secure their data. This chapter explores blockchain's role in strengthening healthcare data security by answering the questions related to what data use, when we need, why we need, who needs, and how state-of-the-art techniques use blockchains to secure healthcare data. As a case study, we also explore and analyze the state-of-the-art implementations for blockchain in healthcare data security for the COVID-19 pandemic. In order to provide a path to future research directions, we identify and discuss the technical limitations and regulatory challenges associated with blockchain-based healthcare data security implementation.

Arun K. Sharma, Nishchal K. Verma

The fault diagnostic model trained for a laboratory case machine fails to perform well on the industrial machines running under variable operating conditions. For every new operating condition of such machines, a new diagnostic model has to be trained which is a time-consuming and uneconomical process. Therefore, we propose a quick learning mechanism that can transform the existing diagnostic model into a new model suitable for industrial machines operating in different conditions. The proposed method uses the Net2Net transformation followed by a fine-tuning to cancel/minimize the maximum mean discrepancy between the new data and the previous one. The fine-tuning of the model requires a very less amount of labelled target samples and very few iterations of training. Therefore, the proposed method is capable of learning the new target data pattern quickly. The effectiveness of the proposed fault diagnosis method has been demonstrated on the Case Western Reserve University dataset, Intelligent Maintenance Systems bearing dataset, and Paderborn university dataset under the wide variations of the operating conditions. It has been validated that the diagnostic model trained on artificially damaged fault datasets can be used to quickly train another model for a real damage dataset.

Vikas Singh, Pooja Agrawal, Teena Sharma et al.

Image denoising is one of the preliminary steps in image processing methods in which the presence of noise can deteriorate the image quality. To overcome this limitation, in this paper a improved two-stage fuzzy filter is proposed for filtering salt and pepper noise from the images. In the first-stage, the pixels in the image are categorized as good or noisy based on adaptive thresholding using type-2 fuzzy logic with exclusively two different membership functions in the filter window. In the second-stage, the noisy pixels are denoised using modified ordinary fuzzy logic in the respective filter window. The proposed filter is validated on standard images with various noise levels. The proposed filter removes the noise and preserves useful image characteristics, i.e., edges and corners at higher noise level. The performance of the proposed filter is compared with the various state-of-the-art methods in terms of peak signal-to-noise ratio and computation time. To show the effectiveness of filter statistical tests, i.e., Friedman test and Bonferroni-Dunn (BD) test are also carried out which clearly ascertain that the proposed filter outperforms in comparison of various filtering approaches.

Nishchal K. Verma, T. Sharma, S. Dixit et al.

This paper introduces a novel toolbox named BIDEAL for the generation of biclusters, their analysis, visualization, and validation. The objective is to facilitate researchers to use forefront biclustering algorithms embedded on a single platform. A single toolbox comprising various biclustering algorithms play a vital role to extract meaningful patterns from the data for detecting diseases, biomarkers, gene-drug association, etc. BIDEAL consists of seventeen biclustering algorithms, three biclusters visualization techniques, and six validation indices. The toolbox can analyze several types of data, including biological data through a graphical user interface. It also facilitates data preprocessing techniques i.e., binarization, discretization, normalization, elimination of null and missing values. The effectiveness of the developed toolbox has been presented through testing and validations on Saccharomyces cerevisiae cell cycle, Leukemia cancer, Mammary tissue profile, and Ligand screen in B-cells datasets. The biclusters of these datasets have been generated using BIDEAL and evaluated in terms of coherency, differential co-expression ranking, and similarity measure. The visualization of generated biclusters has also been provided through a heat map and gene plot.

Vikas Singh, Nishchal K. Verma

In recent years, intelligent condition-based monitor-ing of rotary machinery systems has become a major researchfocus of machine fault diagnosis. In condition-based monitoring,it is challenging to form a large-scale well-annotated datasetdue to the expense of data acquisition and costly annotation.The generated data have a large number of redundant featureswhich degraded the performance of the machine learning models.To overcome this, we have utilized the advantages of minimumredundancy maximum relevance (mRMR) and transfer learningwith a deep learning model. In this work,mRMRis combinedwith deep learning and deep transfer learning framework toimprove the fault diagnostics performance in terms of accuracyand computational complexity. ThemRMRreduces the redundantinformation from data and increases the deep learning perfor-mance, whereas transfer learning, reduces a large amount of datadependency for training the model. In the proposed work, twoframeworks, i.e.,mRMRwith deep learning andmRMRwith deeptransfer learning, have explored and validated on CWRU andIMS rolling element bearings datasets. The analysis shows thatthe proposed frameworks can obtain better diagnostic accuracycompared to existing methods and can handle the data with alarge number of features more quickly.

Vikas Singh, Nishchal K. Verma

This paper presents a new fuzzy k-means algorithm for the clustering of high-dimensional data in various subspaces. Since high-dimensional data, some features might be irrelevant and relevant but may have different significance in the clustering process. For better clustering, it is crucial to incorporate the contribution of these features in the clustering process. To combine these features, in this paper, we have proposed a novel fuzzy k-means clustering algorithm by modifying the objective function of the fuzzy k-means using two different entropy terms. The first entropy term helps to minimize the within-cluster dispersion and maximize the negative entropy to determine clusters to contribute to the association of data points. The second entropy term helps control the weight of the features because different features have different contributing weights during the clustering to obtain a better partition. The proposed approach performance is presented in various clustering measures (AR, RI and NMI) on multiple datasets and compared with six other state-of-the-art methods. Impact Statement- In real-world applications, cluster-dependent feature weights help in partitioning the data set into more meaningful clusters. These features may be relevant, irrelevant, or redundant, but they each have different contributions during the clustering process. In this paper, a cluster-dependent feature weights approach is presented using fuzzy k-means to assign higher weights to relevant features and lower weights to irrelevant features during clustering. The method is validated using both supervised and unsupervised performance measures on real-world and synthetic datasets to demonstrate its effectiveness compared to state-of-the-art methods.