In 2019, the corona virus was found in Wuhan, China. The corona virus has traveled several countries in the world from the beginning of 2020. The early estimation of COVID-19 cases is one of the efficient approaches to control the pandemic. Many researchers had proposed the deep learning model for the efficient estimation of COVID-19 cases for different provinces in the world. The research work had not focused on the discussion of robustness in the model. In this study, centralized federated-convolutional neural network–gated recurrent unit (Fed-CNN–GRU) model is proposed for the estimation of active cases per day in different provinces of India. In India, the uneven transmission of COVID-19 virus was seen in 36 provinces due to the different geographical areas and population densities. So, the methodology of this study had focused on the development of single deep learning algorithm, which is robust and reliable to estimate the active cases of COVID-19 in different provinces of India. The concept of transfer and federated learning is involved to enhance the estimation of active cases of COVID-19 by the CNN–GRU model. The study had considered the active cases per day dataset for 36 provinces in India from 12 March, 2020 to 17 January, 2022. Based on the study, it is proven that the centralized CNN–GRU model by federated learning had captured the transmission dynamics of COVID-19 in different provinces with an enhanced result.

The ability to differentiate sensor-induced physiological signals between healthy and diseased subjects is useful for developing an e-health system. Patients with neurodegenerative disorders are among those who can benefit from the use of e-health. Entropy methods have been utilized to quantify the complexity of such physiological signals for pattern classification. To date, these methods have been applied individually. In this retrospective study, several entropy methods are examined and used as feature extraction methods for machine learning to classify gait patterns in neurodegenerative diseases. Experimental results show that the combination of entropy methods and standard statistical measures performed much better than the individual measures for physiological pattern differentiation. Several machine learning models were also evaluated for learning on these features. This study also found that the one-dimensional convolutional network model trained with the combined features provided the most favorable results, where the best entropy mea-sures depend on certain values for time delays and embedding dimensions.

Abstract Recently, transforming windows files into images and its analysis using machine learning and deep learning have been considered as a state‐of‐the art works for malware detection and classification. This is mainly due to the fact that image‐based malware detection and classification is platform independent, and the recent surge of success of deep learning model performance in image classification. Literature survey shows that convolutional neural network (CNN) deep learning methods are successfully employed for image‐based windows malware classification. However, the malwares were embedded in a tiny portion in the overall image representation. Identifying and locating these affected tiny portions is important to achieve a good malware classification accuracy. In this work, a multi‐headed attention based approach is integrated to a CNN to locate and identify the tiny infected regions in the overall image. A detailed investigation and analysis of the proposed method was done on a malware image dataset. The performance of the proposed multi‐headed attention‐based CNN approach was compared with various non‐attention‐CNN‐based approaches on various data splits of training and testing malware image benchmark dataset. In all the data‐splits, the attention‐based CNN method outperformed non‐attention‐based CNN methods while ensuring computational efficiency. Most importantly, most of the methods show consistent performance on all the data splits of training and testing and that illuminates multi‐headed attention with CNN model's generalizability to perform on the diverse datasets. With less number of trainable parameters, the proposed method has achieved an accuracy of 99% to classify the 25 malware families and performed better than the existing non‐attention based methods. The proposed method can be applied on any operating system and it has the capability to detect packed malware, metamorphic malware, obfuscated malware, malware family variants, and polymorphic malware. In addition, the proposed method is malware file agnostic and avoids usual methods such as disassembly, de‐compiling, de‐obfuscation, or execution of the malware binary in a virtual environment in detecting malware and classifying malware into their malware family.

Summary Industry 4.0 integrates cyber systems, physical devices, and digital networks to automate the industrial process. Many sectors aim to adopt the best practices outlined in Industry 4.0. This indicates well for the future networking of an increasing number of devices. As crucial as intelligent automation is, it is essential that it be protected. The proliferation of Internet‐enabled gadgets could raise vulnerability to a variety of threats, malware among them. Intruders see a synthesis of factors as a chance to carry out their malicious plan. Keeping sensitive data and information protected from malicious software is a high responsibility for all industries. It is critical to have both a trustworthy approach and a large dataset to work with when constructing a malware traffic classifier. Malware's capacity to elude detection by antivirus programs improves with the day. Because this malware has the potential to compromise the entire network, establishing a malware traffic classifier requires a strong approach. As the number of data increases, the classifier has a harder time distinguishing between benign and malicious network entries. As a result, weighing too many factors is a time‐consuming process. To assist with these types of real‐world challenges, we construct an effective hybrid selection component, which is subsequently followed by a neural network classifier in this research. The Malware traffic classifier provided here selects the principal feature using filter and wrapper techniques. The feature columns provided by the feature selection program are used to construct a neural network‐based binary malware classifier. The given malware traffic classification framework was tested using the MTA‐KDD'19 dataset. We set up an experiment in this investigation to examine the way different feature counts perform using a neural‐based classifier. The suggested framework achieves 96.8% accuracy while just considering the bare minimum of five features, which is a substantial increase over alternative methods.

A literature survey shows that the number of malware attacks is gradually growing over the years due to the growing trend of Internet of Medical Things (IoMT) devices. To detect and classify malware attacks, automated malware detection and classification is an essential subsystem in healthcare cyber-physical systems. This work proposes an attention-based multidimensional deep learning (DL) approach for a cross-architecture IoMT malware detection and classification system based on byte sequences extracted from Executable and Linkable Format (ELF; formerly named Extensible Linking Format) files. The DL approach automates the feature design and extraction process from unstructured byte sequences. In addition, the proposed approach facilitates the detection of the central processing unit (CPU) architecture of the ELF file. A detailed experimental analysis and its evaluation are shown on the IoMT cross-architecture benchmark dataset. In all the experiments, the proposed method showed better performance compared with those obtained from several existing methods with an accuracy of 95% for IoMT malware detection, 94% for IoMT malware classification, and 95% for CPU architectures classification. The proposed method also suggests a similar performance with an accuracy of 94% on the Microsoft malware dataset. Experimental results on two malware datasets indicate that the proposed method is robust and generalizable in cross-architecture IoMT malware detection, classification, and CPU architectures classification in healthcare cyber-physical systems.

Rectal or colorectal cancer is one of the leading causes of cancer-related death. With the advancement in surgical techniques, the survival rate has been improved. Predicting the survival rate is an important factor for enabling optimal treatments to prolong rectal-cancer patients' lives. Methods of artificial intelligence and machine learning have been applied for assisting physicians in cancer research. In this study, we investigated the use of pretrained convolutional neural networks and support vector machines for predicting the survival rate of a cohort of rectal-cancer patients using metastatic immunohistochemistry samples staining for protein RhoB. The combination of convolutional neural networks and support vector machines achieved better classification results than using individual pretrained deep networks in most cases, and where manual pathological analysis is encountered with great difficulty. In particular, the combination of ResNet-101 and SVM produced an average accuracy of 86% for non-radiotherapy, and Inception-v3 and SVM resulted in an average accuracy of 85% for radiotherapy.

Abstract Breast cancer is the second most frequent malignant tumor in the world. Early findings of breast cancer can significantly improve treatment effectiveness. Manual methods of breast cancer diagnosis are prone to human fault and inaccuracy, and they take time. A computer‐aided diagnosis can assist radiologists in making better choices by overcoming the disadvantages of manual methods. One of the significant steps in the breast cancer diagnosis process is feature selection. In recent decades, many studies have proposed numerous hybrid optimization methods to select the optimal features in the breast cancer detection system. However, many hybrid optimization algorithms are trapped in local optima and have slow convergence speed. Thus, it reduces the classification accuracy. For resolving these issues, this work proposes a hybrid optimization algorithm that combines the grasshopper optimization algorithm and the crow search algorithm for feature selection and classification of the breast mass with multilayer perceptron. The simulation is experimented with using MATLAB 2019a. The efficacy of the proposed hybrid grasshopper optimization‐crow search algorithm with multilayer perceptron system is compared to multilayer perceptron based algorithms of enhanced and adaptive genetic algorithm, teaching learning‐based whale optimization algorithm, butterfly optimization algorithm, whale optimization algorithm, and grasshopper optimization algorithm. From the results obtained, the proposed grasshopper optimization‐crow search algorithm with the multilayer perceptron method outperforms the comparative models in terms of classification accuracy (97.1%), sensitivity (98%), and specificity (95.4%) for the mammographic image analysis society dataset.

Abstract With the popularity of the internet and smartphones, malware on smartphones has increased dramatically. In addition, the ubiquity and openness of the Android operating system have made it a lucrative platform for cybercriminals to develop malware. Traditional malware detection techniques require a lot of time and manual effort to classify malware accurately. Recently, deep learning (DL) based malware detection and classification techniques have been developed to solve this issue. This article proposes a DL‐based two‐stage framework that detects Android malware and classifies its variants using image‐based malware representations of the Android DEX files. The framework uses the EfficientNetB0 convolutional neural network (CNN) to extracts relevant features from the malware color images. The extracted features are then passed through a global average pooling layer and fed into a stacking classifier. The stacking classifier employs linear support vector machine (SVM) and random forest (RF) algorithms as base‐level classifiers and logistic regression as the meta‐level classifier. This method obtained an accuracy of 100% in the binary classification of Android malware images and a 92.9% accuracy in 5‐class (Adsware, Adware + Adware, Clicker + Trojan, Spyware, and Benign) classification, and an 88.6% accuracy in 4‐class (Adsware, Adware + Adware, Clicker + Trojan, and Spyware) classification. We compared our method with 26 state‐of‐the‐art pretrained CNN models (including the original EfficientNetB0) and large‐scale learning classifiers such as EfficientNetB0‐SVM and EfficientNetB0‐RF. The proposed framework outperformed the compared methods in all performance metrics. Experiments also demonstrate that substituting the softmax layer of CNNs with a large‐scale learning classifier or stacking classifier results in an enhanced performance over the original network.

Abstract Cassava is a rich source of carbohydrates, and it is vulnerable to virus diseases. Literature survey shows that the image recognition and integrated deep learning approach is successfully employed for Cassava leaf disease classification. Mostly, transfer learning based on a convolutional neural network (CNN) models were successfully applied for Cassava leaf disease classification. However, existing approaches are not effective in identifying the tiny portion of the disease in the overall leaf area. Identifying and focussing on regions affected by the disease is vital to achieving a good classification accuracy. An attention‐based approach is integrated into pretrained CNN‐based EfficientNet models to locate and identify the tiny infected regions in Cassava leaf. Penultimate layer features of attention‐based EfficientNet models such as A_EfficientNetB4, A_EfficientNetB5, and A_EfficientNetB6 were extracted. Next, the dimensionality of the extracted features was reduced using kernel principal component analysis. The reduced features were fused and passed into a stacked ensemble meta‐classifier for Cassava leaf disease classification. A stacked ensemble meta‐classifier is a two‐stage approach in which the first stage employs random forest and support vector machine (SVM) for prediction followed by logistic regression for classification. Detailed investigation and analysis of the proposed method, attention, and non‐attention‐based approaches with CNN pretrained models were tested using a publicly available benchmark dataset of Cassava leaf disease images. The proposed method achieved better performances in all experiments than several existing methods as well as various attention and non‐attention‐based CNN pretrained models. The proposed approach can be used as a deployable tool for Cassava leaf disease classification in agricultural field.

Abstract Literature survey shows that convolutional neural network (CNN)‐based pretrained models have been successfully employed to diagnose and detect childhood pneumonia using chest X‐rays (CXR). However, most of the existing methods are prone to imbalance problems, which become even more significant in medical image classification for example most importantly childhood pneumonia classification using CXR. This is due to the fact that some classes in childhood pneumonia have a very little support in the training dataset. Additionally, though the existing methods have reported better performances for training and testing, in most of the test cases the existing models will not be effective on variants of the childhood pneumonia CXR images or CXR samples from a new pediatric patient. In addition, the models may be effective in detecting latent stage pediatric pneumonia but not show better performances for CXR samples from pediatric patients who are early stage, sick but not pneumonia, sick with other lung diseases, and so on. Generalization is an important term to be considered while designing a pneumonia classifier that can perform well on completely unseen pneumonia CXR datasets. This article presents a cost‐sensitive large‐scale learning with stacked ensemble meta‐classifier and transfer learning‐based deep feature fusion approach for pediatric pneumonia classification using CXR. With the aim to identify the importance among the classes of pneumonia, the larger cost items are introduced based on the class‐imbalance degree during the backpropogation learning methodology in transfer learning models such as Xception, InceptionResNetV2, DenseNet201, and NASNetMobile. Next, the features from the penultimate layer (global average pooling) of Xception, InceptionResNetV2, DenseNet201, and NASNetMobile were extracted and dimensionality of the extracted features were reduced using kernel principal component analysis (KPCA). The reduced features were fused together and passed into a stacked ensemble meta‐classifier for classifying the CXR into either pneumonia or normal. A stacked ensemble meta‐classifier is a two stage approach in which the first stage employs random forest and support vector machine (SVM) for prediction and followed by logistic regression for classification. Experiments of the proposed model were done on publicly available benchmark pediatric pneumonia classification CXR dataset. In addition, the experiments for existing methods as well as various cost‐insensitive models were conducted. In all the experiments, the proposed method has achieved better performances compared to the existing methods as well as various cost‐insensitive models. In particular, the proposed method showed 6% improvement in precision, 10% improvement in recall, 9% improvement in F1 score with less misclassification costs (0.0321) and accuracy (96.8%). Most importantly, the proposed method is insensitive to the imbalance data and more effective to handle variants of the childhood pneumonia CXR images. Thus, the proposed approach can be used as a tool for point‐of‐care diagnosis by healthcare professionals.

Abstract Thyroid hormones are essential for all the metabolic and reproductive activities with significance to growth, and neuron development in the human body. The thyroid hormone dysfunction has many ill consequences, affecting the human population; thereby being a global epidemic. It is noticed that every one in 10 persons suffer from different thyroid disorders in India. In recent years, many researchers have implemented various disease predictive models based on Information and Communications Technology (ICT). Increasing the accuracy of disease classification is a critical and challenging task. To increase the accuracy of classification, in this paper, we propose a hybrid optimization algorithm‐based feature selection design for thyroid disease classifier with rough type‐2 fuzzy support vector machine. This work uses the hybrid optimization algorithm, which combines the firefly algorithm (FA) and butterfly optimization algorithm (BOA) to select the top‐n features. The proposed hybrid firefly butterfly optimization‐rough type‐2 fuzzy support vector machine (HFBO‐RT2FSVM) is evaluated with several key metrics such as specificity, accuracy, and sensitivity. We compare our approach with well‐known benchmark methods such as improved grey wolf optimization linear support vector machine (IGWO Linear SVM) and mixed‐kernel support vector machine (MKSVM) methods. From the experimental evaluations, we justify that our technique improves the accuracy by large thereby precise in identifying the thyroid disease. HFBO‐RT2FSVM model attained an accuracy of 99.28%, having specificity and sensitivity of 98 and 99.2%, respectively.

With the exponential increase in a number of web pages daily, it makes it very difficult for a search engine to list relevant web pages. In this paper, we propose a machine learning-based classification model that can learn the best features in each web page and helps in search engine listing. The existing methods for listing have lots of drawbacks like interfacing the normal operations of the website and crawling lots of useless information. Our proposed algorithm provides an optimal classification for websites which has a large number of web pages such as Wikipedia by just considering core information like link text, side information, and header text. We implemented our algorithm with standard benchmark datasets, and the results show that our algorithm outperforms the existing algorithms.