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The feature selection problem is a significant challenge in pattern recognition, especially for classification tasks. The quality of the selected features plays a critical role in building effective models, and poor-quality data can make this process more difficult. This work explores the use of association analysis in data mining to select meaningful features, addressing the issue of duplicated information in the selected features. A novel feature selection technique for text classification is proposed, based on frequent and correlated items. This method considers both relevance and feature interactions, using association as a metric to evaluate the relationship between the target and features. The technique was tested using the SMS spam collecting dataset from the UCI machine learning repository and compared with well-known feature selection methods. The results showed that the proposed technique effectively reduced redundant information while achieving high accuracy (95.155%) using only 6% of the features.

COVID-19 is a virus that causes upper respiratory tract and lung infections. The number of cases and deaths increased daily during the pandemic. Once it is vital to diagnose such a disease in a timely manner, the researchers have focused on computer-aided diagnosis systems. Chest X-rays have helped monitor various lung diseases consisting COVID-19. In this study, we proposed a deep transfer learning approach with novel fine-tuning mechanisms to classify COVID-19 from chest X-ray images. We presented one classical and two new fine-tuning mechanisms to increase the model's performance. Two publicly available databases were combined and used for the study, which included 3616 COVID-19 and 1576 normal (healthy) and 4265 pneumonia X-ray images. The models achieved average accuracy rates of 95.62%, 96.10%, and 97.61%, respectively, for 3-class cases with fivefold cross-validation. Numerical results show that the third model reduced 81.92% of the total fine-tuning operations and achieved better results. The proposed approach is quite efficient compared with other state-of-the-art methods of detecting COVID-19.

This paper presents a review on methods for class-imbalanced learning with the Support Vector Machine (SVM) and its variants. We first explain the structure of SVM and its variants and discuss their inefficiency in learning with class-imbalanced data sets. We introduce a hierarchical categorization of SVM-based models with respect to class-imbalanced learning. Specifically, we categorize SVM-based models into re-sampling, algorithmic, and fusion methods, and discuss the principles of the representative models in each category. In addition, we conduct a series of empirical evaluations to compare the performances of various representative SVM-based models in each category using benchmark imbalanced data sets, ranging from low to high imbalanced ratios. Our findings reveal that while algorithmic methods are less time-consuming owing to no data pre-processing requirements, fusion methods, which combine both re-sampling and algorithmic approaches, generally perform the best, but with a higher computational load. A discussion on research gaps and future research directions is provided.

The epidemic situation may cause severe social and economic impacts on a country. So, there is a need for a trustworthy prediction model that can offer better prediction results. The forecasting result will help in making the prevention policies and remedial action in time, and thus, we can reduce the overall social and economic impacts on the country. This article introduces a CNN-LSTM hybrid deep learning prediction model, which can correctly forecast the COVID-19 epidemic across India. The proposed model uses convolutional layers, to extract meaningful information and learn from a given time series dataset. It is also enriched with the LSTM layer's capability, which means it can identify long-term and short-term dependencies. The experimental evaluation has been performed to gauge the performance and suitability of our proposed model among the other well-established time series forecasting models. From the empirical analysis, it is also clear that the use of extra convolutional layers with the LSTM layer may increase the forecasting model's performance. Apart from this, the deep insides of the current situation of medical resource availability across India have been discussed.

In this study, the flood hazards susceptibility map of an area in Turkey which is frequently exposed to flooding was predicted by training 70% of inventory data. For this, statistical, and hybrid methods such as frequency ratio (FR), evidential belief function (EBF), weight of evidence (WoE), index of entropy (IoE), fuzzy logic (FL), principal component analysis (PCA), analytical hierarchy process (AHP), technique for order preference by similarity to an ideal solution (TOPSIS), and VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR) were adapted. Values at both 70% and 30% of inventory data from the generated maps were extracted to validate the training and testing processes by receiver operating characteristics (ROC) analysis and seed cell area index (SCAI). Sensitivity, specificity, accuracy, and kappa index were calculated from ROC analysis, and SCAI was computed from the classification of map by natural break method and flood pixels in that classification. Since the predicted results of the methods applied did not point out the same model for each criterion, a simple method was selected to determine the most preferable method. Analysis showed that, IoE model was found to be the best model considering the ROC parameters, while PCA and AHP methods gave more reliable results considering SCAI. This study may be considered as a comprehensive contribution to the hybridization methods in predicting accurate flood hazards susceptibility maps.

Nowadays, the number of sudden deaths due to heart disease is increasing with the coronavirus pandemic. Therefore, automatic classification of electrocardiogram (ECG) signals is crucial for diagnosis and treatment. Thanks to deep learning algorithms, classification can be performed without manual feature extraction. In this study, we propose a novel convolutional neural networks (CNN) architecture to detect ECG types. In addition, the proposed CNN can automatically extract features from images. Here, we classify a real ECG dataset using our proposed CNN which includes 34 layers. While this dataset is one-dimensional signals, these are transformed into images (scalograms) using continuous wavelet transform (CWT). In addition, the proposed CNN is compared to known architectures: AlexNet and SqueezeNet for classifying ECG images, and we find it more effective than others. This study, which not only performed CWT but also implemented short-time Fourier transform, examines the success in recognizing ECG types for the proposed CNN. Besides, different split methods: training and testing, and cross-validation are applied in this study. Eventually, CWT and cross-validation are the best pre-processing and split methods for the proposed CNN, respectively. Although the results are quite good, we benefit from support vector machines (SVM) to obtain the best algorithm and for detecting ECG types. Essentially, the main aim of the study increases classification results. In this way, the proposed CNN is utilized as deep feature extractor and combined with SVM. As a conclusion of this study, we achieve the highest accuracy of 99.21% from the proposed CNN–SVM when using CWT. Therefore, we can express that this framework can be used as an aid to clinicians for ECG-type identification.

The advent of Healthcare 5.0 heralds a groundbreaking revolution in digital healthcare, superseding the achievements of its predecessor, Healthcare 4.0. Integrating cutting-edge technologies such as the Internet of Medical Things (IoMT), smart wearables, and the extraordinary capabilities of Artificial Intelligence (AI), Healthcare 5.0 envisions a unified framework that grants seamless access to health records, fosters interconnectedness among individuals, resources, and institutions, and empowers intelligent responses to medical concerns. However, the realization of Healthcare 5.0 faces a significant challenge in the form of high-speed data transmission using smart devices. Conventional AI approaches relying on centralized data processing raise compelling concerns surrounding information privacy and scalability within the Healthcare 5.0 context. Amidst this backdrop, federated learning emerges as a beacon of hope, offering a decentralized AI paradigm that facilitates on-device machine learning without compromising end-user privacy through centralized data export. Safeguarding data integrity, federated learning holds the key to unlocking the full potential of Healthcare 5.0. In this pioneering study, we conduct an extensive survey, exploring the transformative implications of federated learning within the realm of Healthcare 5.0. By shedding light on recent advancements tailored to this paradigm, we delve into the fundamental concepts of resource-awareness, privacy preservation, incentivization, and personalization, all within the framework of federated learning. Moreover, we meticulously scrutinize key parameters including security, sparsification, quantization, robustness, scalability, and privacy, providing an authentic evaluation of the current progress in federated learning for Healthcare 5.0. This comprehensive survey serves as an indispensable cornerstone for the evolution of Healthcare 5.0, offering invaluable insights into its unique requirements and untapped potential. By harnessing the capabilities of federated learning in this context, we envisage a transformative era in digital healthcare, fostering a more interconnected, secure, and intelligent healthcare landscape for the betterment of society.

Predicting volatility is a critical activity for taking risk- adjusted decisions in asset trading and allocation. In order to provide effective decision-making support, in this paper we investigate the profitability of a deep Long Short-Term Memory (LSTM) Neural Network for forecasting daily stock market volatility using a panel of 28 assets representative of the Dow Jones Industrial Average index combined with the market factor proxied by the SPY and, separately, a panel of 92 assets belonging to the NASDAQ 100 index. The Dow Jones plus SPY data are from January 2002 to August 2008, while the NASDAQ 100 is from December 2012 to November 2017. If, on the one hand, we expect that this evolutionary behavior can be effectively captured adaptively through the use of Artificial Intelligence (AI) flexible methods, on the other, in this setting, standard parametric approaches could fail to provide optimal predictions. We compared the volatility forecasts generated by the LSTM approach to those obtained through use of widely recognized benchmarks models in this field, in particular, univariate parametric models such as the Realized Generalized Autoregressive Conditionally Heteroskedastic (R-GARCH) and the Glosten–Jagannathan–Runkle Multiplicative Error Models (GJR-MEM). The results demonstrate the superiority of the LSTM over the widely popular R-GARCH and GJR-MEM univariate parametric methods, when forecasting in condition of high volatility, while still producing comparable predictions for more tranquil periods.

Three-class brain tumor classification becomes a contemporary research task due to the distinct characteristics of tumors. The existing proposals employ deep neural networks for the three-class classification. However, achieving high accuracy is still an endless challenge in brain image classification. We have proposed a deep dense inception residual network for three-class brain tumor classification. We have customized the output layer of Inception ResNet v2 with a deep dense network and a softmax layer. The deep dense network has improved the classification accuracy of the proposed model. The proposed model has been evaluated using key performance metrics on a publicly available brain tumor image dataset having 3064 images. Our proposed model outperforms the existing model with a mean accuracy of 99.69%. Further, similar performance has been obtained on noisy data.

In recent years deep learning models improve the diagnosis performance of many diseases especially respiratory diseases. This paper will propose an evaluation for the performance of different deep learning models associated with the raw lung auscultation sounds in detecting respiratory pathologies to help in providing diagnostic of respiratory pathologies in digital recorded respiratory sounds. Also, we will find out the best deep learning model for this task. In this paper, three different deep learning models have been evaluated on non-augmented and augmented datasets, where two different datasets have been utilized to generate four different sub-datasets. The results show that all the proposed deep learning methods were successful and achieved high performance in classifying the raw lung sounds, the methods were applied on different datasets and used either augmentation or non-augmentation. Among all proposed deep learning models, the CNN–LSTM model was the best model in all datasets for both augmentation and non-augmentation cases. The accuracy of CNN–LSTM model using non-augmentation was 99.6%, 99.8%, 82.4%, and 99.4% for datasets 1, 2, 3, and 4, respectively, and using augmentation was 100%, 99.8%, 98.0%, and 99.5% for datasets 1, 2, 3, and 4, respectively. While the augmentation process successfully helps the deep learning models in enhancing their performance on the testing datasets with a notable value. Moreover, the hybrid model that combines both CNN and LSTM techniques performed better than models that are based only on one of these techniques, this mainly refers to the use of CNN for automatic deep features extraction from lung sound while LSTM is used for classification.