A Survey on Regression-Based Crowd Counting Techniques

Investigating the learning approaches of students in nursing education

Fruit recognition and nutrition detection consumes time and requires labor while using traditional methods. Deep learning can acquire robust features from images that's why it is used for this application. In this paper, feature extraction from fruit images is done using six different deep learning approaches. Then the fruit images are classified into different fruit categories and the nutritional content is estimated for each fruit image. The six deep learning approaches are CNN 15-layer, CNN 8-layer, VGG16, ResNet18, ResNet50, and InceptionV3 applied on 18 fruit types. To overcome the limitations in each dataset on its own, two datasets, Fruit-360 and Fruits and Vegetables Image Recognition were combined. Four optimizers are employed for evaluating each deep learning approach. These optimizers are Adam, Nadam, RMSprop, and SGD. This study highlights the importance of using diverse optimization algorithms within each deep learning approach and combining datasets to enhance the fruit recognition models accuracy. Results showed that the 15- layer CNN model achieved the highest accuracy for both fruit recognition and nutrition detection at 99.12%. This paper shows the potential of deep learning in automizing fruit recognition and nutrition detection which saves time and labor in food industry. These findings will promote healthy eating habits and facilitate dietary management.

Deep learning (DL) is now generally acknowledged as the benchmark and evolution in machine learning (ML) fields. Further more, it has steadily suited the most extensively in computational techniques for ML, delivering excellent outcomes in several challenging intellectual works that have equal or still outperformed human ability. DL has the advantage of learning from enormous amounts of data. Here, the DL approach has been applied to classify thunderstorms and non-thunderstorms with a meteorological dataset. Daily observational hourly data sets from 2016 to 2021 have been used to classify the incidence of thunderstorms and non-thunderstorms. Three DL approaches are used: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) to classify thunderstorm incidences. These DL techniques are compared with conventional ML techniques, showing that DL approaches outperformed ML. The CNN method provides the highest performance with 92.01% accuracy among all DL and ML approaches.

Anomaly detection framework for Internet of things traffic using vector convolutional deep learning approach in fog environment

This study aimed to explore the prognostic impact of spatial distribution of tumor‐infiltrating lymphocytes (TILs) quantified by deep learning (DL) approaches based on digitalized whole‐slide images stained with hematoxylin and eosin in patients with colorectal cancer (CRC). The prognostic impact of spatial distributions of TILs in patients with CRC was explored in the Yonsei cohort (n = 180) and validated in The Cancer Genome Atlas (TCGA) cohort (n = 268). Two experienced pathologists manually measured TILs at the most invasive margin (IM) as 0–3 by the Klintrup–Mäkinen (KM) grading method and this was compared to DL approaches. Inter‐rater agreement for TILs was measured using Cohen's kappa coefficient. On multivariate analysis of spatial TIL features derived by DL approaches and clinicopathological variables including tumor stage, microsatellite instability, and KRAS mutation, TIL densities within 200 μm of the IM (f_im200) remained the most significant prognostic factor for progression‐free survival (PFS) (hazard ratio [HR] 0.004 [95% confidence interval, CI, 0.0001–0.15], p = 0.0028) in the Yonsei cohort. On multivariate analysis using the TCGA dataset, f_im200 retained prognostic significance for PFS (HR 0.031 [95% CI 0.001–0.645], p = 0.024). Inter‐rater agreement of manual KM grading was insignificant in the Yonsei (κ = 0.109) and the TCGA (κ = 0.121) cohorts. The survival analysis based on KM grading showed statistically significant different PFS in the TCGA cohort, but not the Yonsei cohort. Automatic quantification of TILs at the IM based on DL approaches shows prognostic utility to predict PFS, and could provide robust and reproducible TIL density measurement in patients with CRC.

In the last few years, Brain Computer Interfaces (BCI) attempted the attention of many researchers. In Motor Imagery (MI)-BCI, central nervous system directly connected to a computer or an automation system. Characteristics of the electromyographic (EEG) signals are utilized in MI-BCI systems. Various techniques have been proposed to extract EEG signal characteristics during recent years. The main objective of this research is to employ an efficient deep learning approach to extract the features of EEG signals using composition of convolutional Neural Network and discrete wavelet transform utilized in the BCI system. The deep learning approach presented in this study has rarely been explored to employ for EEG features extraction. The simulation study indicates that the presented method carry out remarkable accuracy and high performance compared with conventional approaches such as support vector machine and artificial Neural Network methods and give a powerful indicative decision making aid to assist physicians in the treatment of the right and left-hand features for real time motor imagery classification system. Furthermore, the most advantages of employing the proposed method are to eliminate the feature selection level and reducing the processing cost significantly.

More and more deep learning approaches have been proposed to segment secondary structures from cryo-electron density maps at medium resolution range (5-10Å). Although the deep learning approaches show great potential, only a few small experimental data sets have been used to test the approaches. There is limited understanding about potential factors, in data, that affect the performance of segmentation. We propose an approach to generate data sets with desired specifications in three potential factors - the protein sequence identity, structural contents, and data quality. The approach was implemented and has generated a test set and various training sets to study the effect of secondary structure content and data quality on the performance of DeepSSETracer, a deep learning method that segments regions of protein secondary structures from cryo-EM map components. Results show that various content levels in the secondary structure and data quality influence the performance of segmentation for DeepSSETracer.

Posture detection targeted towards providing assessments for the monitoring of health and welfare of pigs has been of great interest to researchers from different disciplines. Existing studies applying machine vision techniques are mostly based on methods using three-dimensional imaging systems, or two-dimensional systems with the limitation of monitoring under controlled conditions. Thus, the main goal of this study was to determine whether a two-dimensional imaging system, along with deep learning approaches, could be utilized to detect the standing and lying (belly and side) postures of pigs under commercial farm conditions. Three deep learning-based detector methods, including faster regions with convolutional neural network features (Faster R-CNN), single shot multibox detector (SSD) and region-based fully convolutional network (R-FCN), combined with Inception V2, Residual Network (ResNet) and Inception ResNet V2 feature extractions of RGB images were proposed. Data from different commercial farms were used for training and validation of the proposed models. The experimental results demonstrated that the R-FCN ResNet101 method was able to detect lying and standing postures with higher average precision (AP) of 0.93, 0.95 and 0.92 for standing, lying on side and lying on belly postures, respectively and mean average precision (mAP) of more than 0.93.

Sleep stage classification is important to accurately predict and diagnose patients with sleep disorders. Though various deep learning approaches have been implemented to classify sleep classes, these consist limitations that impact the accuracy and processing time of the classification model. The aim of this research is to enhance the accuracy and minimize the training time of the deep learning classification model. The proposed system consists of One Dimensional Convolutional Neural Network (CNN) with enhanced loss function to improve the accuracy of scoring of five different sleep classes. Preprocessing, Feature Extraction and Classification are the main components of the proposed system. Initially, EEG signals are fed to an adaptive filter for preprocessing, in order to remove any noise in signal. Thereafter, feature is extracted through multiple convolutional and pooling layers, and finally the classification is done by fully connected layer using softmax activation with enhanced loss function. The proposed solution is tested on data samples from multiple datasets with five classes of Sleep classification. Based on the obtained results, the proposed solution has found to achieve an accuracy of 96.26% which is almost 4.2% higher than the state-of-the-art solution which is 92.76%. Furthermore, the processing time has been reduced by 11 milliseconds against the state-of-the-art solution. The proposed system focused on classifying sleep stages in five classes using EEG signals with deep learning approach. It enhances the loss function in order to minimize errors in the prediction of sleep classes and improves the accuracy of the model. Furthermore, the training speed of the model has also been reduced by applying batch normalization techniques inside the model. In the future, larger datasets of different sleep disorder patients with varying features can be used for training and implementing the proposed solution. The datasets can also be pre-processed using additional techniques to refine the data before feeding to the neural network model.

Cervical cancer is the second most common cancer affecting women worldwide but is curable if diagnosed early. Routinely, expert pathologists visually examine histology slides for assessing cervix tissue abnormalities. A localized, fusion-based, hybrid imaging and deep learning approach is explored to classify squamous epithelium into cervical intraepithelial neoplasia (CIN) grades for a dataset of 83 digitized histology images. Partitioning the epithelium region into 10 vertical segments, 27 handcrafted image features and rectangular patch, sliding window-based convolutional neural network features are computed for each segment. The imaging and deep learning patch features are combined and used as inputs to a secondary classifier for individual segment and whole epithelium classification. The hybrid method achieved a 15.51% and 11.66% improvement over the deep learning and imaging approaches alone, respectively, with a 80.72% whole epithelium CIN classification accuracy, showing the enhanced epithelium CIN classification potential of fusing image and deep learning features.

Cervical cancer is the second most common cancer affecting women worldwide but is curable if diagnosed early. Routinely, expert pathologists visually examine histology slides for assessing cervix tissue abnormalities. A localized, fusion-based, hybrid imaging and deep learning approach is explored to classify squamous epithelium into cervical intraepithelial neoplasia (CIN) grades for a dataset of 83 digitized histology images. Partitioning the epithelium region into 10 vertical segments, 27 handcrafted image features and rectangular patch, sliding window-based convolutional neural network features are computed for each segment. The imaging and deep learning patch features are combined and used as inputs to a secondary classifier for individual segment and whole epithelium classification. The hybrid method achieved a 15.51% and 11.66% improvement over the deep learning and imaging approaches alone, respectively, with a 80.72% whole epithelium CIN classification accuracy, showing the enhanced epithelium CIN classification potential of fusing image and deep learning features.

The relationship between students’ learning approaches and academic performance is a critical factor in higher education. This study examined the association between learning approaches and academic achievement among undergraduate students enrolled in an organic chemistry course. A cross-sectional survey design was employed, involving a purposive sample of 36 Level 200 undergraduate chemistry students (28 males, 8 females). Data were collected using the Revised Two-Factor Study Process Questionnaire (R-SPQ-2F). Pearson’s correlation analysis revealed a weak, negative, and non-significant relationship between deep learning approaches and academic performance (r = –0.132), as well as between surface approaches and performance (r = –0.273). These findings suggest that neither deep nor surface learning approaches significantly predict academic outcomes in organic chemistry. The study highlights the need for pedagogical practices that promote reflective learning, intrinsic motivation, and deep engagement with course content.

Respiratory motion induces artifacts in reconstructed cardiac perfusion SPECT images. Correction for respiratory motion often relies on a respiratory signal describing the heart displacements during breathing. However, using external tracking devices to estimate respiratory signals can add cost and operational complications in a clinical setting. We aim to develop a deep learning (DL) approach that uses only SPECT projection data for respiratory signal estimation. A modified U-Net was implemented that takes temporally finely sampled SPECT sub-projection data (100ms) as input. These sub-projections are obtained by reframing the 20-s list-mode data, resulting in 200 sub-projections, at each projection angle for each SPECT camera head. The network outputs a 200-time-point motion signal for each projection angle, which was later aggregated over all angles to give a full respiratory signal. The target signal for DL model training was from an external stereo-camera visual tracking system (VTS). In addition to comparing DL and VTS, we also included a data-driven approach based on the center-of-mass (CoM) strategy. This CoM method estimates respiratory signals by monitoring the axial changes of CoM for counts in the heart region of the sub-projections. We utilized 900 subjects with stress cardiac perfusion SPECT studies, with 302 subjects for testing and the remaining 598 subjects for training and validation. The Pearson's correlation coefficient between the DL respiratory signal and the reference VTS signal was 0.90, compared to 0.70 between the CoM signal and the reference. For respiratory motion correction on SPECT images, all VTS, DL, and CoM approaches partially de-blured the heart wall, resulting in a thinner wall thickness and increased recovered maximal image intensity within the wall, with VTS reducing blurring the most followed by the DL approach. Uptake quantification for the combined anterior and inferior segments of polar maps showed a mean absolute difference from the reference VTS of 1.7% for the DL method for patients with motion >12mm, compared to 2.6% for the CoM method and 8.5% for no correction. We demonstrate the capability of a DL approach to estimate respiratory signal from SPECT projection data for cardiac perfusion imaging. Our results show that the DL based respiratory motion correction reduces artefacts and achieves similar regional quantification to that obtained using the stereo-camera VTS signals. This may enable fully automatic data-driven respiratory motion correction without relying on external motion tracking devices.

Imaging of the secondary electron bremsstrahlung (SEB) x rays emitted during particle-ion irradiation is a promising method for beam range estimation. However, the SEB x-ray images are not directly correlated to the dose images. In addition, limited spatial resolution of the x-ray camera and low-count situation may impede correctly estimating the beam range and width in SEB x-ray images. To overcome these limitations of the SEB x-ray images measured by the x-ray camera, a deep learning (DL) approach was proposed in this work to predict the dose images for estimating the range and width of the carbon ion beam on the measured SEB x-ray images. To prepare enough data for the DL training efficiently, 10,000 simulated SEB x-ray and dose image pairs were generated by our in-house developed model function for different carbon ion beam energies and doses. The proposed DL neural network consists of two U-nets for SEB x ray to dose image conversion and super resolution. After the network being trained with these simulated x-ray and dose image pairs, the dose images were predicted from simulated and measured SEB x-ray testing images for performance evaluation. For the 500 simulated testing images, the average mean squared error (MSE) was 2.5×10-5 and average structural similarity index (SSIM) was 0.997 while the error of both beam range and width was within 1mm FWHM. For the three measured SEB x-ray images, the MSE was no worse than 5.5×10-3 and SSIM was no worse than 0.980 while the error of the beam range and width was 2mm and 5mm FWHM, respectively. We have demonstrated the advantages of predicting dose images from not only simulated data but also measured data using our deep learning approach.

Achieving energy efficiency (EE) fairness among heterogeneous mobile devices will become a crucial issue in future wireless networks. This paper investigates a deep learning (DL) approach for improving EE fairness performance in interference channels (IFCs) where multiple transmitters simultaneously convey data to their corresponding receivers. To improve the EE fairness, we aim to maximize the minimum EE among multiple transmitter–receiver pairs by optimizing the transmit power levels. Due to fractional and max-min formulation, the problem is shown to be non-convex, and, thus, it is difficult to identify the optimal power control policy. Although the EE fairness maximization problem has been recently addressed by the successive convex approximation framework, it requires intensive computations for iterative optimizations and suffers from the sub-optimality incurred by the non-convexity. To tackle these issues, we propose a deep neural network (DNN) where the procedure of optimal solution calculation, which is unknown in general, is accurately approximated by well-designed DNNs. The target of the DNN is to yield an efficient power control solution for the EE fairness maximization problem by accepting the channel state information as an input feature. An unsupervised training algorithm is presented where the DNN learns an effective mapping from the channel to the EE maximizing power control strategy by itself. Numerical results demonstrate that the proposed DNN-based power control method performs better than a conventional optimization approach with much-reduced execution time. This work opens a new possibility of using DL as an alternative optimization tool for the EE maximizing design of the next-generation wireless networks.