MAPE values of index stocks in convolution kernels with different numbers (%).

Mask R-CNN on NYUv2

This repository mainly contains information from the execution of the Mask R-CNN network [1] on images from the NYUv2 dataset [2] as well as additional metadata. It was created for analyzing the output of Mask R-CNN and post-processing it using contextual information for improving its performance. This work has been carried out by Dr. Jose-Raul Ruiz-Sarmiento (MAPIR group, University of Málaga) and Dr. Shuda Li (AVG group, University of Oxford) in the scope of the European project MoveCare: Multiple-actOrs Virtual Empathic CARgiver for the Elder (Ref: 732158).

Concretely, this repository includes:

• metadata:
  

  • coco_nyu_mapping.txt: Mapping between the categories in COCO dataset and those in NYUv2.
    
  • coco_object_categories.txt: Object categories considered in COCO dataset.
    
  • nyu_object_categories.txt: Object categories used in NYUv2 dataset.
    
  • nyu_scene_categories.txt: Scene categories considered in NYUv2.
    
  • objects_and_categories_in_images.txt: For each image in NYUv2, the categories of the appearing objects.

• nyu_content:
  

  • masks_in_X (Where X is the image index)
    
    • Y.png: Where Y is the object index in the image, represents the binary mask of that object.
      
    • pixels_labelled.png: Binary mask indicating the labelled pixels in image X.
      
  • bboxesX.txt: Where X is the image index, includes the ground truth bounding boxes of the objects in it. Format is: min_x min_y max_x max_y.

• preds:
  

  • X: Where X is the image index.
    
    • Y.png: Where Y is the object index in the image, as detected by Mask R-CNN. Binary image containing the mask of such detected object.
      
  • X.txt: Where X is the image index. File containing the objects detected by Mask R-CNN, including: idx class score min_x min_y max_x max_y masks_file, being min_x min_y max_x and max_y bounding box information, while masks_file refers to X/Y.png as described above.
    
  • result_X.png: Where X is the image index. Image showing the detections with a socre higher than 0.3.
    
  • gt_iou_X: Where X is the image index.
    
    • Y: Where Y is the index of the detected object.
      
      • Z.png Where Z is the index of the object in the ground truth. Image showing the masks of both objects, Y and Z, for visually checking their overlapping.
        
    • Y.txt: Where Y is the index of the detected object. File containing:
      
      • The intersection ratio of the object mask Y with the labelled part of the image.
        
      • The IoU value for the mask of object Y and those of ground truth objects.
        
        
        References:

[1] He, Kaiming, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. "Mask r-cnn." In Proceedings of the IEEE international conference on computer vision, pp. 2961-2969. 2017.
[2] Silberman, Nathan, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. "Indoor segmentation and support inference from rgbd images." In European Conference on Computer Vision, pp. 746-760. Springer, Berlin, Heidelberg, 2012.

MAPE value of index stocks (%).

In a survey held in May 2025, 12 percent of Americans said that they found CNN to be a very trustworthy source of news and information. Opinions regarding the network’s trustworthiness varied, with 20 percent of respondents saying that they did not trust CNN at all. CNN CNN is a pay television channel founded in the 1980s, and is currently owned by AT&T. It is one of the most popular news networks in the U.S. and has spawned successful affiliate networks around the world. Survey responses to attitudes about the credibility of CNN found that 50 percent of all participating adults deemed CNN to be very or somewhat credible, though opinions differed among certain groups. The station is viewed by some as more liberal-leaning in terms of political spin, and as such political affiliation affected respondents' feelings about the channel. Age, educational background, ethnicity, and multiple other factors also influenced attitudes to CNN's credibility, as is the case with competing channels such as MSNBC and Fox News. Fake news The credibility of news sources is of growing importance to consumers as more and more news audiences are faced with fake news, which is an ongoing issue not only in the United States but also in other countries around the world. Fake news puts the public at risk of digesting deliberately untrue or inaccurate information about major happenings and events. News consumers are increasingly concerned about the spread and influence of fake news, so much so that the share of adults who would support government intervention in restricting false information grew by seven percent between 2021 and 2023.

View market daily updates and historical trends for CBOE Equity Put/Call Ratio. from United States. Source: Chicago Board Options Exchange. Track economic…

R2 of index stocks.

This dataset is designed for research and experimentation in deep learning–based spectrum sensing and reconstruction for Cognitive Radio Networks (CRNs). It provides synthetic data representing wideband signal sensing under varying Signal-to-Noise Ratio (SNR) and Compression Ratio (CR) conditions, making it ideal for training Convolutional Neural Networks (CNNs) or U-Net architectures for compressed sensing signal recovery and primary user detection tasks.

The dataset simulates the compressed sensing acquisition process using different SNR levels and compression ratios, followed by signal reconstruction quality metrics such as Mean Squared Error (MSE), Reconstructed SNR, Detection Probability, and False Alarm Rate.

This dataset can be used to:

Train CNNs or U-Nets for compressed spectrum reconstruction

Evaluate model robustness across varying noise and compression levels

Benchmark deep learning-based spectrum sensing against traditional methods

Column Name Description Data Type Example Instance Unique sample index Integer 1 SNR (dB) Input signal-to-noise ratio Float -5 CR (Compression Ratio) Ratio of compressed to original samples Float 0.8 True Label (PU Active=1/Inactive=0) Indicates if the Primary User (PU) is active Integer (0/1) 1 Reconstructed SNR (dB) SNR after CNN/U-Net signal reconstruction Float 4.7 MSE Mean Squared Error between original and reconstructed signals Float 0.009 Detection Probability Probability of correctly detecting PU activity Float 0.96 False Alarm Rate Probability of falsely detecting PU activity Float 0.06

Specification Value Number of Samples 10,000 Compression Ratios Used 0.2, 0.4, 0.6, 0.8 SNR Range (dB) -10 dB to +10 dB Label Distribution Balanced (Active/Inactive ≈ 50:50) File Format CSV File Size ~1.2 MB Number of Features 7 (excluding Instance index)

Cite these papers :

Cite these papers

[1] Kadhiravan, D., Pradeepa, J., & Ragavan, K. (2024). Remote Disease Diagnosis through IoMT-Enhanced Blood Cell Classification with Deep Learning. The Open Biomedical Engineering Journal, 18(1). [2] Kadhiravan, D., Sharmila, A., & Pradeepa, J. INVESTIGATION OF OPTIMIZING EFFICIENCY ON ORIENTATION EFFECTS ON WIRELESS POWER TRANSFER. [3] Darly, S. S., Kadhiravan, D., Hemachandran, K., & Rege, M. (2024). Simulation strategies for analyzing of data. Handbook of Artificial Intelligence and Wearables, 27-64.

Diese Statistik stellt anhand des American Customer Satisfaction Index (ACSI)* auf einer Skala von 0 bis 100 die Zufriedenheit der Nutzer mit CNN.com in den USA in den Jahren 2011 bis 2022 dar. Im Jahr 2022 erreichte der ACSI für CNN.com einen Wert von 69 Punkten.

Some data of Shenzhen Composite Index.

The first column lists the notation of each NN. The network structures (MSp, MRp, CSp, and CRp) used in the previous experiments are highlighted. The column Layers lists the number of fully connected layers of MLP and the number of convolutional layers for CNN. The column Units/Filters lists the number of units in the hidden layers of MLP and the number of filters in the convolutional layers of CNN. The columns Ours and Baseline list the cumulative assets obtained by the target NNs over the entire test period.

BackgroundThe convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as Alzheimer's disease (AD). AD is a degenerative disease difficult to clinical diagnosis due to its unclear underlying pathological mechanism. Previous studies have primarily focused on investigating structural abnormalities in the brain's functional networks related to the AD or proposing different deep learning approaches for AD classification.ObjectiveThe aim of this study is to leverage the advantages of combining brain topological features extracted from functional network exploration and deep features extracted by the CNN. We establish a novel fMRI-based classification framework that utilizes Resting-state functional magnetic resonance imaging (rs-fMRI) with the phase synchronization index (PSI) and 2D-CNN to detect abnormal brain functional connectivity in AD.MethodsFirst, PSI was applied to construct the brain network by region of interest (ROI) signals obtained from data preprocessing stage, and eight topological features were extracted. Subsequently, the 2D-CNN was applied to the PSI matrix to explore the local and global patterns of the network connectivity by extracting eight deep features from the 2D-CNN convolutional layer.ResultsFinally, classification analysis was carried out on the combined PSI and 2D-CNN methods to recognize AD by using support vector machine (SVM) with 5-fold cross-validation strategy. It was found that the classification accuracy of combined method achieved 98.869%.ConclusionThese findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional connections, and characterize brain functional abnormalities, which could effectively detect AD anomalies by the extracted features that may provide new insights into exploring the underlying pathogenesis of AD.

MAPE value of index stock obtained by different methods (%).

Correlation coefficients of Euclidean distance and index measures with the rank order of the sampled CNN layers from each scene-trained CNNs for the different image collections.

A lower index number indicates lower in-class variance and greater between-class variance (overall better performance).

Banking and stock markets consider gold to be an important component of their economic and financial status. There are various factors that influence the gold price trend and its fluctuations. Accurate and reliable prediction of the gold price is an essential part of financial and portfolio management. Moreover, it could provide insights about potential buy and sell points in order to prevent financial damages and reduce the risk of investment. In this paper, different architectures of deep neural network (DNN) have been proposed based on long short-term memory (LSTM) and convolutional-based neural networks (CNN) as a hybrid model, along with automatic parameter tuning to increase the accuracy, coefficient of determination, of the forecasting results. An illustrative dataset from the closing gold prices for 44 years, from 1978 to 2021, is provided to demonstrate the effectiveness and feasibility of this method. The grid search technique finds the optimal set of DNNs’ parameters. Furthermore, to assess the efficiency of DNN models, three statistical indices of RMSE, RMAE, and coefficient of determination (R2), were calculated for the test set. Results indicate that the proposed hybrid model (CNN-Bi-LSTM) outperforms other models in total bias, capturing extreme values and obtaining promising results. In this model, CNN is used to extract features of input dataset. Furthermore, Bi-LSTM uses CNN’s outputs to predict the daily closing gold price.

Today, with the rapid growth of Internet technology, the changing trend of real estate finance has brought great an impact on the progress of the social economy. In order to explore the visual identification (VI) effect of Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) algorithm based on neural network optimization on China’s real estate index and stock trend, in this study, artificial neural network (ANN) algorithm is introduced to predict its trend. Firstly, LSTM algorithm can effectively solve the problem of vanishing gradient, which is suitable for dealing with the problems related to time series. Secondly, CNN, with its unique fine-grained convolution operation, has significant advantages in classification problems. Finally, combining the LSTM algorithm with the CNN algorithm, and using the Bayesian Network (BN) layer as the transition layer for further optimization, the CNN-LSTM algorithm based on neural network optimization has been constructed for the VI and prediction model of real estate index and stock trend. Through the performance verification of the model, the results reveal that the CNN-LSTM optimization algorithm has a more accurate prediction effect, the prediction accuracy is 90.55%, and the prediction time is only 52.05s. At the same time, the significance advantage of CNN-LSTM algorithm is verified by statistical method, which can provide experimental reference for intelligent VI and prediction of trend of China real estate index and property company stocks.

The descriptive statistics of the CNN, LSTM, and CNN-LSTM algorithms.

RMSE and its average values under CNN, LSTM and CNN-LSTM algorithms.

ANOVA test results of the proposed CNN-LSTM, CNN, and LSTM algorithms.

Vegetation indices.