Computer Vision Technology Based on Sensor Data and Hybrid Deep Learning for Security Detection of Blast Furnace Bearing

The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.

Forecasting day-ahead electricity prices is a crucial research area. Both wholesale and retail sectors highly value improved forecast accuracy. Renewable energy sources have grown more influential and effective in the US power market. However, current forecasting models have shortcomings, including inadequate consideration of renewable energy impacts and insufficient feature selection. Many studies lack reproducibility, clear presentation of input features, and proper integration of renewable resources. This study addresses these gaps by incorporating a comprehensive set of input features, while these features are engineered to capture complex market dynamics. The model’s unique aspect is its inclusion of renewable-related inputs, such as temperature data for solar energy effects and wind speed for wind energy impacts on US electricity prices. The research also employs data preprocessing techniques like windowing, cleaning, normalization, and feature engineering to enhance input data quality and relevance. We developed four advanced hybrid deep learning models to improve electricity price prediction accuracy and reliability. Our approach combines variational mode decomposition (VMD) with four deep learning (DL) architectures: dense neural networks (DNNs), convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and bidirectional LSTM (BiLSTM) networks. This integration aims to capture complex patterns and time-dependent relationships in electricity price data. Among these, the VMD-BiLSTM model consistently outperformed the others across all window implementations. Using 24 input features, this model achieved a remarkably low mean absolute error of 0.2733 when forecasting prices in the MISO market. Our research advances electricity price forecasting, particularly for the US energy market. These hybrid deep neural network models provide valuable tools and insights for market participants, energy traders, and policymakers.

Activity recognition and localization in outdoor environments involve identifying and tracking human movements using sensor data, computer vision, or deep learning techniques. This process is crucial for applications such as smart surveillance, autonomous systems, healthcare monitoring, and human–computer interaction. However, several challenges arise in outdoor settings, including varying lighting conditions, occlusions caused by obstacles, environmental noise, and the complexity of differentiating between similar activities. This study presents a hybrid deep learning approach that integrates human activity recognition and localization in outdoor environments using Wi-Fi signal data. The study focuses on applying the hybrid long short-term memory–bi-gated recurrent unit (LSTM-BIGRU) architecture, designed to enhance the accuracy of activity recognition and location estimation. Moreover, experiments were conducted using a real-world dataset collected with the PicoScene Wi-Fi sensing device, which captures both magnitude and phase information. The results demonstrated a significant improvement in accuracy for both activity recognition and localization tasks. To mitigate data scarcity, this study utilized the conditional tabular generative adversarial network (CTGAN) to generate synthetic channel state information (CSI) data. Additionally, carrier frequency offset (CFO) and cyclic shift delay (CSD) preprocessing techniques were implemented to mitigate phase fluctuations. The experiments were conducted in a line-of-sight (LoS) outdoor environment, where CSI data were collected using the PicoScene Wi-Fi sensor platform across four different activities at outdoor locations. Finally, a comparative analysis of the experimental results highlights the superior performance of the proposed hybrid LSTM-BIGRU model, achieving 99.81% and 98.93% accuracy for activity recognition and location prediction, respectively.

Deep learning, a subset of artificial intelligence, gives easy way for the analytical and physical tasks to be done automatically. There is a less necessity for human intervention while performing these tasks. Deep hybrid learning is a blended approach to combine machine learning with deep learning. A hybrid deep learning (HDL) model using convolutional neural network (CNN), residual network (ResNet) and long short term memory (LSTM) is proposed for better course selection of the enrolled candidates in an online learning platform. In this work, a hybrid framework that facilitates the analysis and design of a recommendation system for course selection is developed. A student’s schedule for the next course should consist of classes in which the student has shown interest. For universities to schedule classes optimally, they need to know what courses each student wants to take before each course begins. The proposed recommendation system selects the most appropriate course that can encourage students to base their selection on informed decision making. This system will enable learners to obtain the correct choices of courses to be studied.

e16550 Background: Improved computational power and modern algorithms have generated significant interest in radiomics for cancer diagnosis and staging. Here we assess the performance of deep learning (DL) models as a means for feature extraction in combination with supervised machine learning (ML) algorithms for accurate staging and chemotherapy response assessment of bladder cancer. Methods: Deidentified grayscale CT images from bladder cancer patients scheduled to undergo radical cystectomy were included in this retrospective study. These images were manually annotated with two regional masks (normal region and cancer region). Five DL models- namely, AlexNet, GoogleNet, InceptionV3, ResNet-50, and XceptionNet pre-trained on the ImageNet dataset, a public dataset, were then fine-tuned on our bladder CT scan data to extract features. Through feature selection process, the subset of the features was used to build ML classifiers for classification. The classification was performed using five different ML classifiers, namely k-Nearest Neighbor (KNN), Naïve-Bayes (NB), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Decision Tree (DT). The classification task was performed with 10-fold cross-validation, and each of the experiments contained a different but not mutually exclusive subset of samples. The evaluation metrics include accuracy, sensitivity, specificity, precision, and F1-score. Results: A total of 200 deidentified grayscale CT images of 100 patients with histologically proven bladder cancer, were included in this study. For experiment (1) normal vs. cancer, the LDA classifier on XceptionNet based features provides the best performance with an accuracy of 86.07%, sensitivity of 96.75%, specificity of 69.65%, precision of 83.07%, and F1-score of 89.39%. For experiment (2) non-muscle invasive Cancer (NMIBC) vs. muscle invasive bladder cancer (MIBC), the LDA classifier on XceptionNet based features provided the best performance with an accuracy of 79.72%, sensitivity of 66.62%, specificity of 87.39%, precision of 75.58%, and F1-score of 70.81%. For experiment (3) T0 lesion vs. MIBC, the LDA classifier on XceptionNet based features provides the best performance with an accuracy of 74.96%, sensitivity of 80.51%, specificity of 70.22%, precision of 69.78%, and F1-score of 74.73%. Conclusions: Our proposed model has shown good results in differentiating normal vs cancer and promising performance in differentiating T0 vs MIBC after chemotherapy treatment. We are expanding our dataset to further improve the performance in differentiating T0 vs MIBC. In addition, we will investigate the applicability of GAN for data augmentation to address data limit. We believe the hybrid DL and ML framework may facilitates radiologists' decisions and clinical decision-making in patients with bladder cancer.

ObjectiveAccurate segmentation of the lung nodule in computed tomography images is a critical component of a computer‐assisted lung cancer detection/diagnosis system. However, lung nodule segmentation is a challenging task due to the heterogeneity of nodules. This study is to develop a hybrid deep learning (H‐DL) model for the segmentation of lung nodules with a wide variety of sizes, shapes, margins, and opacities.Materials and methodsA dataset collected from Lung Image Database Consortium image collection containing 847 cases with lung nodules manually annotated by at least two radiologists with nodule diameters greater than 7 mm and less than 45 mm was randomly split into 683 training/validation and 164 independent test cases. The 50% consensus consolidation of radiologists' annotation was used as the reference standard for each nodule. We designed a new H‐DL model combining two deep convolutional neural networks (DCNNs) with different structures as encoders to increase the learning capabilities for the segmentation of complex lung nodules. Leveraging the basic symmetric U‐shaped architecture of U‐Net, we redesigned two new U‐shaped deep learning (U‐DL) models that were expanded to six levels of convolutional layers. One U‐DL model used a shallow DCNN structure containing 16 convolutional layers adapted from the VGG‐19 as the encoder, and the other used a deep DCNN structure containing 200 layers adapted from DenseNet‐201 as the encoder, while the same decoder with only one convolutional layer at each level was used in both U‐DL models, and we referred to them as the shallow and deep U‐DL models. Finally, an ensemble layer was used to combine the two U‐DL models into the H‐DL model. We compared the effectiveness of the H‐DL, the shallow U‐DL and the deep U‐DL models by deploying them separately to the test set. The accuracy of volume segmentation for each nodule was evaluated by the 3D Dice coefficient and Jaccard index (JI) relative to the reference standard. For comparison, we calculated the median and minimum of the 3D Dice and JI over the individual radiologists who segmented each nodule, referred to as M‐Dice, min‐Dice, M‐JI, and min‐JI.ResultsFor the 164 test cases with 327 nodules, our H‐DL model achieved an average 3D Dice coefficient of 0.750 ± 0.135 and an average JI of 0.617 ± 0.159. The radiologists' average M‐Dice was 0.778 ± 0.102, and the average M‐JI was 0.651 ± 0.127; both were significantly higher than those achieved by the H‐DL model (p < 0.05). The radiologists' average min‐Dice (0.685 ± 0.139) and the average min‐JI (0.537 ± 0.153) were significantly lower than those achieved by the H‐DL model (p < 0.05). The results indicated that the H‐DL model approached the average performance of radiologists and was superior to the radiologist whose manual segmentation had the min‐Dice and min‐JI. Moreover, the average Dice and average JI achieved by the H‐DL model were significantly higher than those achieved by the individual shallow U‐DL model (Dice of 0.745 ± 0.139, JI of 0.611 ± 0.161; p < 0.05) or the individual deep U‐DL model alone (Dice of 0.739 ± 0.145, JI of 0.604 ± 0.163; p < 0.05).ConclusionOur newly developed H‐DL model outperformed the individual shallow or deep U‐DL models. The H‐DL method combining multilevel features learned by both the shallow and deep DCNNs could achieve segmentation accuracy comparable to radiologists' segmentation for nodules with wide ranges of image characteristics.

Background: The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0HDL (AtheroPoint™, Roseville, CA, USA) was designed to demonstrate how well the features obtained from carotid plaques determine the risk of CVD. We hypothesize that hybrid deep learning (HDL) will outperform unidirectional deep learning, bidirectional deep learning, and machine learning (ML) paradigms. Methodology: 500 people who had undergone targeted carotid B-mode ultrasonography and coronary angiography were included in the proposed study. ML feature selection was carried out using three different methods, namely principal component analysis (PCA) pooling, the chi-square test (CST), and the random forest regression (RFR) test. The unidirectional and bidirectional deep learning models were trained, and then six types of novel HDL-based models were designed for CVD risk stratification. The AtheroEdge™ 3.0HDL was scientifically validated using seen and unseen datasets while the reliability and statistical tests were conducted using CST along with p-value significance. The performance of AtheroEdge™ 3.0HDL was evaluated by measuring the p-value and area-under-the-curve for both seen and unseen data. Results: The HDL system showed an improvement of 30.20% (0.954 vs. 0.702) over the ML system using the seen datasets. The ML feature extraction analysis showed 70% of common features among all three methods. The generalization of AtheroEdge™ 3.0HDL showed less than 1% (p-value < 0.001) difference between seen and unseen data, complying with regulatory standards. Conclusions: The hypothesis for AtheroEdge™ 3.0HDL was scientifically validated, and the model was tested for reliability and stability and is further adaptable clinically.

Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review

Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy

Underwater images often suffer from poor clarity and color distortion due to light absorption and dispersion. This paper proposes a Hybrid Deep Learning-based Vanished Object Detection method to enhance underwater image quality and improve object detection accuracy. In the preprocessing stage, Shade-of-Grey and Max-RGB techniques are combined to correct lighting distortions. A hybrid deep learning model, integrating CNN and RNN, is used for image enhancement and object classification based on underwater visual traits. To further improve object tracking and recognition, a Kalman Filter (KF) is incorporated into the deep learning structure. The method is implemented using Python and evaluated on both synthetic and real underwater datasets. Performance is measured using precision, recall, accuracy, F1-score, runtime, and RMSE. Results show a significant improvement in color accuracy and visual quality, with the proposed model achieving up to 99.1% accuracy at a learning rate of 80, outperforming existing techniques.

Today, smartphones are used by the majority of internet users worldwide, and Android has become the most popular smartphone operating system on the market. The growth in the use of smartphones in general, and the Android system specifically, results in a stronger requirement to successfully protect Android, as malware developers aim to create advanced and sophisticated malware applications. Cybercriminals utilize fraudulent attack tactics, namely obfuscation or dynamic code triggering, to evade the system. A standard static investigation method failed to recognize such attacks. Mitigating a wide variety of evasive attacks requires a refined, dynamic, and analytical framework. Conventional artificial intelligence (AI), particularly machine learning (ML) methodologies, are no longer effective in detecting all new and complex malware types. A deep learning (DL) model, which is very different from conventional ML models, has a possible solution to the detection issue of each version of malware. In this manuscript, an Approach for Improving Malware Detection Performance Using a Hybrid Deep Learning Framework (IMDP-HDL) is proposed. The primary objective of the IMDP-HDL methodology is to ensure the effective and scalable deployment of malware detection in real-world cybersecurity environments. Initially, the Z-score standardization is utilized to ensure consistent feature scaling and model performance. For the malware detection process, a hybrid model combining a convolutional neural network, bi-directional long short-term memory, and self-attention mechanism (CBiLSTM-SA) is employed. A broad range of experimentation with the IMDP-HDL model is performed using the Android malware dataset. The comparison analysis of the IMDP-HDL model demonstrated a superior accuracy value of 99.22% over existing techniques.

Sign language recognition (SLR) contains the capability to convert sign language gestures into spoken or written language. This technology is helpful for deaf persons or hard of hearing by providing them with a way to interact with people who do not know sign language. It is also be utilized for automatic captioning in live events and videos. There are distinct methods of SLR comprising deep learning (DL), computer vision (CV), and machine learning (ML). One general approach utilises cameras for capturing the signer's hand and body movements and processing the video data for recognizing the gestures. One of challenges with SLR comprises the variability in sign language through various cultures and individuals, the difficulty of certain signs, and require for realtime processing. This study introduces an Automated Sign Language Detection and Classification using Reptile Search Algorithm with Hybrid Deep Learning (SLDC-RSAHDL). The presented SLDC-RSAHDL technique detects and classifies different types of signs using DL and metaheuristic optimizers. In the SLDC-RSAHDL technique, MobileNet feature extractor is utilized to produce feature vectors, and its hyperparameters can be adjusted by manta ray foraging optimization (MRFO) technique. For sign language classification, the SLDC-RSAHDL technique applies HDL model, which incorporates the design of Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). At last, the RSA was exploited for the optimal hyperparameter selection of the HDL model, which resulted in an improved detection rate. The experimental result analysis of the SLDC-RSAHDL technique on sign language dataset demonstrates the improved performance of the SLDC-RSAHDL system over other existing DL techniques.

Although accurate wind power prediction can improve the reliability, security and economic operation of a power system, the prediction task is complex due to the intermittent nature of wind speed its strong dependence on weather conditions. This paper proposes a novel framework, consisting of hybrid deep learning models, an optimization algorithm and a data decomposition technique, to improve the forecasting accuracy of ultra-short-term wind power generation. The data of the wind power generation collected from a real wind farm are preprocessed and decomposed using a variational mode decomposition (VMD). A deep-learning model (long short-term memory (LSTM) with dropout regularization) is designed to accurately predict the decomposed spectra. The hyper-parameters of the model are optimized by applying the grey-wolf optimization (GWO) algorithm to choose the best hyper-parameters. The combination of deep learning and optimization algorithm plays a key role in achieving better prediction accuracy. The effectiveness of the proposed framework is measured by applying it to two data sets, and the framework is compared with other forecasting models, such as a hybrid deep-learning and empirical wavelet transform (EWT) with LSTM. Comparison with experimental results demonstrates that the novel hybrid framework has the best prediction accuracy in forecasting ultra short-term wind power generation.

INTRODUCTION: The conversion of forests into human lands causes the intrusion of wild animals into the residential area. There is a necessity to prevent the intrusion of such wild animals which causes damage to properties and harm or kill humans. Human population growth leads to an increase in the exploitation of forest areas and related resources for residential and other settlement purposes. There is a need for a system to detect the entry of such animals into habitats. OBJECTIVES: This paper proposes that conversion of forests into human lands causes the intrusion of wild animals into the residential area. METHODS: Deep learning technology combined with Internet of Things (IoT) devices can be deployed in the process of restricting the entry of wild animals into residential areas. The proposed system uses deep learning techniques with the use of various algorithms like DenseNet 201, ResNet50 and You Only Look Once (YOLO). These deep-learning algorithms predict wild animals through image classification. This is done using IoT devices placed in such areas. The role of IoT devices is to transmit the computer vision images to the deep learning module, receive the output, and alarm the residents of the area. RESULTS: The main results are implementation prediction of animals for image processing Datasets used for the prediction and classification indulge the use of cloud modules. It stores the dataset for the prediction process and transfers it whenever needed. As the proposed system is a hybrid model that uses more than one algorithm, the accuracy obtained from the prediction for DenseNet 201, ResNet50 and You Only Look Once (YOLO) algorithm is 82%,92%, and 98%. CONCLUSION: The prediction of those animals is done by a deep learning model which comprises three algorithms are DenseNet 201, ResNet50 and YOLOv3. Comparing the accuracy of an algorithm with higher accuracy is considered efficient and accurate.

Indoor activity monitoring methods promise the wellbeing and security of elderly and visually challenging individuals living in their homes. These methods use numerous technologies and sensors to monitor daily actions, namely movement, sleep patterns, and medication compliance, presenting valued opinions of the consumer’s daily life and complete health. The accuracy and adaptability of the deep learning (DL) model make human activity recognition (HAR) a critical device to improve effectiveness, security, and modified understandings in indoor areas. Using DL techniques, HAR transforms indoor monitoring by permitting particular detection and experience of human actions. DL techniques automatically remove and learn discriminating characteristics, making them suitable for identifying composite human activities in sensor data. Nevertheless, selecting the appropriate DL architecture and enhancing its parameters was important for superior solutions. This study presents a novel Indoor Activity Monitoring for Visually Impaired People with Hybrid Deep Learning for Enhanced Safety (IAMVIP-HDLES) methodology for IoT environments. The IAMVIP-HDLES methodology is designed to monitor and recognize indoor activities of visually impaired people in a real-time environment. The IAMVIP-HDLES approach primarily performs Z-score normalization as a data pre-processing technique for standardizing the input data, guaranteeing uniformity, and improving the system’s performance. For feature selection, the Osprey–Cauchy-sparrow search algorithm (OCSSA) is employed to identify the most appropriate features from the raw data. In addition, the hybrid method, which is comprised of a convolutional neural network, bidirectional long short-term memory, and attention mechanisms (CNN-BiLSTM-Attention), is used for monitoring indoor activities. Finally, the hyperparameter selection process based on the improved whale optimization algorithm (IWOA) is performed to enhance the classification results of the CNN-BiLSTM-Attention method. Experimental studies are performed, and the results are inspected using the UCI-HAR dataset. The comparative analysis of the IAMVIP-HDLES method specified a superior accuracy value of 96.76% over existing techniques.