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COVID-19, a novel pathogen that emerged in late 2019, has the potential to cause pneumonia with unique variants upon infection. Hence, the development of efficient diagnostic systems is crucial in accurately identifying infected patients and effectively mitigating the spread of the disease. However, the system poses several challenges because of th...
Citations
... DL is capable of capturing complex interactions and forecasting price fluctuation in terms of historical and speculative stocks [12]. Owing to this, DL has played an important role in a variety of fields such as cancer diagnosis [13,14], detection of viral infection [15][16][17], cybersecurity [18,19], and intelligent transportation [20,21]. ...
... Improved generalization ability and decreased complexity are correlated with a lower value of Ω f t ( ). Equation (16) shows that L is the number of leaf nodes, ω is the score awarded to a leaf node, γ controls the number of leaf nodes, and λ limits the scores of leaf nodes to avoid unnecessarily high values. ...
... state the objective function of XGBoost. Ω f i ( ) is the regularization term in Equations(15)and(16). ...
Forecasting speculative stock prices is essential for effective investment risk management and requires innovative algorithms. However, the speculative nature, volatility, and complex sequential dependencies within financial markets present inherent challenges that necessitate advanced techniques. In this regard, a novel framework, ACB-XDE (Attention-Customized BiLSTM-XGB Decision Ensemble), is proposed for predicting the daily closing price of speculative stock Bitcoin-USD (BTC-USD). The proposed ACB-XDE framework integrates the learning capabilities of a customized Bi-directional Long Short-Term Memory (BiLSTM) model with a novel attention mechanism and the XGBoost algorithm. The customized BiLSTM leverages its learning capabilities to capture complex sequential dependencies and speculative market trends. Meanwhile, the new attention mechanism dynamically assigns weights to influential features based on volatility patterns, thereby enhancing interpretability and optimizing effective cost measures and volatility forecasting. Moreover, XGBoost handles nonlinear relationships and contributes to the proposed ACB-XDE framework’s robustness. Furthermore, the error reciprocal method improves predictions by iteratively adjusting model weights based on the difference between theoretical expectations and actual errors in the individual attention-customized BiLSTM and XGBoost models. Finally, the predictions from both the XGBoost and attention-customized BiLSTM models are concatenated to create a varied prediction space, which is then fed into the ensemble regression framework to improve the generalization capabilities of the proposed ACB-XDE framework. Empirical validation of the proposed ACB-XDE framework involves its application to the volatile Bitcoin market, utilizing a dataset sourced from Yahoo Finance (Bitcoin-USD, 10/01/2014 to 01/08/2023). The proposed ACB-XDE framework outperforms state-of-the-art models with a MAPE of 0.37%, MAE of 84.40, and RMSE of 106.14. This represents improvements of approximately 27.45%, 53.32%, and 38.59% in MAPE, MAE, and RMSE respectively, over the best-performing attention-BiLSTM. The proposed ACB-XDE framework presents a technique for informed decision-making in dynamic financial landscapes and demonstrates effectiveness in handling the complexities of BTC-USD data.
... The dataset was split into 80% training, 10% testing, and 10% validation sets, with images resized to 256×256. During testing, the system achieved 76% accuracy,81.1% sensitivity, 61.5% specificity, and an AUC of 81.9%. The multi-view fusion model outperformed single-view models. ...
Background and Objective
Computed tomography (CT) imaging plays a crucial role in the early detection and diagnosis of life-threatening diseases, particularly in respiratory illnesses and oncology. The rapid advancement of deep learning (DL) has revolutionized CT image analysis, enhancing diagnostic accuracy and efficiency. This review explores the impact of advanced DL methodologies in CT imaging, with a particular focus on their applications in coronavirus disease 2019 (COVID-19) detection and lung nodule classification.
Methods
A comprehensive literature search was conducted, examining the evolution of DL architectures in medical imaging from conventional convolutional neural networks (CNNs) to sophisticated foundational models (FMs). We reviewed publications from major databases, focusing on developments in CT image analysis using DL from 2013 to 2023. Our search criteria included all types of articles, with a focus on peer-reviewed research papers and review articles in English.
Key Content and Findings
The review reveals that DL, particularly advanced architectures like FMs, has transformed CT image analysis by streamlining interpretation processes and enhancing diagnostic capabilities. We found significant advancements in addressing global health challenges, especially during the COVID-19 pandemic, and in ongoing efforts for lung cancer screening. The review also addresses technical challenges in CT image analysis, including data variability, the need for large high-quality datasets, and computational demands. Innovative strategies such as transfer learning, data augmentation, and distributed computing are explored as solutions to these challenges.
Conclusions
This review underscores the pivotal role of DL in advancing CT image analysis, particularly for COVID-19 and lung nodule detection. The integration of DL models into clinical workflows shows promising potential to enhance diagnostic accuracy and efficiency. However, challenges remain in areas of interpretability, validation, and regulatory compliance. The review advocates for continued research, interdisciplinary collaboration, and ethical considerations as DL technologies become integral to clinical practice. While traditional imaging techniques remain vital, the integration of DL represents a significant advancement in medical diagnostics, with far-reaching implications for future research, clinical practice, and healthcare policy.
... Attackers continually develop new techniques to crack encryption and access protected data. As shown in Figure 1, cryptanalysis methods can sometimes reveal encrypted information, emphasizing the ongoing battle between enhancing encryption methods and adapting to the evolving strategies of attackers [6][7][8][9][10]. Data security is vital for protecting sensitive information. ...
... DL systems have the potential to streamline the diagnosis process for healthcare professionals, but their effectiveness relies on the availability of reliable data. Notably, constructing a dataset for a novel pandemic poses significant challenges, as highlighted by [58], [59]. To address this challenge, we undertook the collection of MPox patient data from diverse sources, including platforms like Kaggle [60], iStock, newspapers, and publicly available samples obtained through Google searches. ...
Monkeypox (MPox) has emerged as a significant global concern, with cases steadily increasing daily. Conventional detection methods, including polymerase chain reaction (PCR) and manual examination, exhibit challenges of low sensitivity, high cost, and substantial workload. Therefore, deep learning offers an automated solution; however, the datasets include data scarcity, texture, contrast, inter-intra class variability, and similarities with other skin infectious diseases. In this regard, a novel hybrid approach is proposed that integrates the learning capacity of Residual Learning and Spatial Exploitation Convolutional Neural Network (CNN) with a customized Swin Transformer (RS-FME-SwinT) to capture multi-scale global and local correlated features for MPox diagnosis. The proposed RS-FME-SwinT technique employs a transfer learning-based feature map enhancement (FME) technique, integrating the customized SwinT for global information capture, residual blocks for texture extraction, and spatial blocks for local contrast variations. Moreover, incorporating new inverse residual blocks within the proposed SwinT effectively captures local patterns and mitigates vanishing gradients. The proposed RS-FME-SwinT has strong learning potential of diverse features that systematically reduce intra-class MPox variation and enable precise discrimination from other skin diseases. Finally, the proposed RS-FME-SwinT is a holdout cross-validated on a diverse MPox dataset and achieved outperformance on state-of-the-art CNNs and ViTs. The proposed RS-FME-SwinT demonstrates commendable results of an accuracy of 97.80%, sensitivity of 96.82%, precision of 98.06%, and an F-score of 97.44% in MPox detection. The RS-FME-SwinT could be a valuable tool for healthcare practitioners, enabling prompt and accurate MPox diagnosis and contributing significantly to mitigation efforts.
... Softmax is employed for class probability assignment in both classification and segmentation tasks. The 95% confidence interval (CI) for sensitivity and the area under curve (AUC) of detection models is computed [51,52]. MATLAB 2023b is used on an Intel Core i7 processor and Nvidia GTX 1080 Tesla GPU-enabled system. ...
COVID-19 poses a global health crisis, necessitating precise diagnostic methods for timely containment. However, accurately delineating COVID-19-affected regions in lung CT scans is challenging due to contrast variations and significant texture diversity. In this regard, this study introduces a novel two-stage classification and segmentation CNN approach for COVID-19 lung radiological pattern analysis. A novel Residual-BRNet is developed to integrate boundary and regional operations with residual learning, capturing key COVID-19 radiological homogeneous regions, texture variations, and structural contrast patterns in the classification stage. Subsequently, infectious CT images undergo lesion segmentation using the newly proposed RESeg segmentation CNN in the second stage. The RESeg leverages both average and max-pooling implementations to simultaneously learn region homogeneity and boundary-related patterns. Furthermore, novel pixel attention (PA) blocks are integrated into RESeg to effectively address mildly COVID-19-infected regions. The evaluation of the proposed Residual-BRNet CNN in the classification stage demonstrates promising performance metrics, achieving an accuracy of 97.97%, F1-score of 98.01%, sensitivity of 98.42%, and MCC of 96.81%. Meanwhile, PA-RESeg in the segmentation phase achieves an optimal segmentation performance with an IoU score of 98.43% and a dice similarity score of 95.96% of the lesion region. The framework’s effectiveness in detecting and segmenting COVID-19 lesions highlights its potential for clinical applications.
... Due to the powerful focal processor that may be integrated into the control board, the framework is capable of performing complex computations and concentrating signal processing in order to identify fire signatures. A number of distinct addressable sensors will have the capability to exchange information and provide the panels with sensitivity levels that differ, in order to facilitate processing and decision-making [50][51][52][53]. Due to its alternative design, the sensor potentially possesses critical information that could be utilized for early fire detection, thereby augmenting the capabilities of fire detection while reducing the overall cost of the system. ...
... The depicted configuration can be observed in the block diagram presented in Figure 6. The line-follower robot was controlled by reading the infrared sensors and utilising the four DC motors; the speed of the left and right motors was established utilising a PID controller and the predicted error; the system was implemented on the Arduino Uno to ensure that the robot moved in the intended direction; and error prediction was accomplished by utilising the infrared sensors for line detection [13][14][15]. ...
... Softmax is employed for class probability assignment in both classification and segmentation tasks. The 95% confidence interval (CI) for sensitivity and the Area Under Curve (AUC) of detection models is computed [49], [50]. MATLAB 2023b is used on an Intel Core i7 processor and Nvidia GTX 1080 Tesla GPU-enabled system. ...
COVID-19 poses a global health crisis, necessitating precise diagnostic methods for timely containment. However, accurately delineating COVID-19 affected regions in Lung CT scans is challenging due to contrast variations and significant texture diversity. In this regard, this study introduces a novel two-stage classification and segmentation CNN approach for COVID-19 lung radiological pattern analysis. A novel Residual-BRNet is developed to integrate boundary and regional operations with residual learning, capturing key COVID-19 radiological homogeneous regions, texture variations, and structural contrast patterns in the classification stage. Subsequently, infectious CT images undergo lesion segmentation in the second stage using the newly proposed RESeg segmentation CNN. The RESeg leverages both average and max-pooling implementations to simultaneously learn region homogeneity and boundary-related patterns. Furthermore, novel pixel attention (PA) blocks are integrated into RESeg to effectively address mildly infected regions. The evaluation of the proposed Residual-BRNet CNN demonstrates promising performance metrics, achieving an accuracy of 97.97%, F1-score of 98.01%, sensitivity of 98.42%, and MCC of 96.81%. Meanwhile, PA-RESeg achieves optimal segmentation performance with an IoU score of 98.43% and a Dice Similarity score of 95.96% of the lesion region. These findings highlight the potential of the proposed diagnosis framework to assist radiologists in identifying and analyzing COVID-19 affected lung regions.The CAD GUI diagnosis tool is provided at https://github.com/PRLAB21/COVID-19-Diagnostic-System.
... 2.Internal cause: Internal threats are one of the most common internal sources of data loss. from the inside out Threats are authorised personnel who can willfully abuse their powers and conduct maliciously and send critical data outside the organization's network [25][26][27][28][29][30][31][32][33][34][35][36]. ...
... The attention mechanism, denoted by 'LA' and 'σ,' employs activation. Here, q, v, ' represent query, value, and transposed key matrices, respectively (Equation 11). Additionally, √ serves as a scaling parameter, with ' ' representing the dimension of the key matrix. ...
Alzheimer diseases (ADs) involves cognitive decline and abnormal brain protein accumulation, necessitating timely diagnosis for effective treatment. Therefore, CAD systems leveraging deep learning advancements have demonstrated success in AD detection but pose computational intricacies and the dataset minor contrast, structural, and texture variations. In this regard, a novel hybrid FME-Residual-HSCMT technique is introduced, comprised of residual CNN and Transformer concepts to capture global and local fine-grained AD analysis in MRI. This approach integrates three distinct elements: a novel CNN Meet Transformer (HSCMT), customized residual learning CNN, and a new Feature Map Enhancement (FME) strategy to learn diverse morphological, contrast, and texture variations of ADs. The proposed HSCMT at the initial stage utilizes stem convolution blocks that are integrated with CMT blocks followed by systematic homogenous and structural (HS) operations. The customized CMT block encapsulates each element with global contextual interactions through multi-head attention and facilitates computational efficiency through lightweight. Moreover, inverse residual and stem CNN in customized CMT enables effective extraction of local texture information and handling vanishing gradients. Furthermore, in the FME strategy, residual CNN blocks utilize TL-based generated auxiliary and are combined with the proposed HSCMT channels at the target level to achieve diverse enriched feature space. Finally, diverse enhanced channels are fed into a novel spatial attention mechanism for optimal pixel selection to reduce redundancy and discriminate minor contrast and texture inter-class variation. The proposed achieves an F1-score (98.55%), an accuracy of 98.42% and a sensitivity of 98.50%, a precision of 98.60% on the standard Kaggle dataset, and demonstrates outperformance existing ViTs and CNNs methods.
