Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The benchmark interest rate in Philippines was last recorded at 5.25 percent. This dataset provides the latest reported value for - Philippines Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Abstract: The dataset was recorded during milling of 16MnCr5. Due to artificially introduced, though realistic anoma-lies in the workpiece the dataset can be applied for anomaly detection. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learning. TechnicalRemarks: The dataset consists of seven folders. Each folder represents one milling run. In each milling run the depth of cut was set to 3 mm. A folder contains a maximum of three json files. The number of files depends on the time needed for each run which is a function of milling tool diameter and feed rate. Files in each folder were numerated in sequence. For example, folder “run1” contains the files “run1_1” and “run1_2” with the last number indicating the order in which the files were generated. The frequency of recording datapoints was set to 500 Hz. During each milling run the milling tool moved along the longitudinal side and then was moved back alongside the workpiece. This way machining started always on the same side of the workpiece. Table 1 provides an overview of the milling runs. Run 1 to 4 were performed with a HSS tool with a diameter of 10 mm. The tool in use was an end mill (HSS-E-SPM HPC 10 mm) developed by Hoffmann Group. During the first three runs with this end mill no tool breakage occurred. However, in run 4 the tool broke. Runs 5 and 6 were performed by milling with an end mill of the same tool series (HSS-E-SPM HPC 8 mm) that just differs in tool diameter. In contrast to this run 7 was performed by using a solid carbid tool (Solid carbide roughing end mill HPC 8 mm). Cutting with SC tools provides much higher productivity with the downside being higher tool price. In our case the SC end mill performed cuts with a feed rate of 1150 mm/min compared to 191 mm/min achieved by a HSS end mill of the same diameter. Tool breakages were recorded on all runs with end mills of diameter 8 mm. Table 1. overview of the data folders folder name | number of json files | tool diameter | tool breakage | tool type run 1 2 10 mm No HSS run 2 2 10 mm No HSS run 3 2 10 mm No HSS run 4 2 10 mm Yes HSS run 5 2 8 mm Yes HSS run 6 3 8 mm Yes HSS run 7 1 8 mm Yes SC Each json file consists of a header and a payload. The header lists all parameters that were recorded such as position, motor torque and motor current of each of a maximum of five axes of a milling machine. However, the machine used in our experiments is a 3-axis machining center which leaves the payload of 2 possible additional axes to be empty. In the payload the sequential data for each parameter can be found. A list of recorded signals can be found in Table 2. Table 2. recorded signals during milling Signal index in payload | Signal name | Signal Address |Type 13-18 VelocityFeedForward VEL_FFW|1 double 19-24 Power POWER|1 string 25-30 CountourDeviation CONT_DEV|1 double 38-43 TorqueFeedForward TORQUE_FFW|1 double 44-49 Encoder1Position ENC1_POS|1 double 56-61 Load LOAD|1 double 68-73 Torque TORQUE|1 double 68-91 Current CURRENT|1 double 1 represents x-axis, 2 represents y-axis, 3 represents z-axis and 6 represents spindle-axis. Note that our milling center has 3 axis and therefore values for axes 4 and 5 are null.
Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
License information was derived automatically
This dataset is part of the following publication at the TransAI 2023 conference: R. Wallsberger, R. Knauer, S. Matzka; "Explainable Artificial Intelligence in Mechanical Engineering: A Synthetic Dataset for Comprehensive Failure Mode Analysis" DOI: http://dx.doi.org/10.1109/TransAI60598.2023.00032
This is the original XAI Drilling dataset optimized for XAI purposes and it can be used to evaluate explanations of such algortihms. The dataset comprises 20,000 data points, i.e., drilling operations, stored as rows, 10 features, one binary main failure label, and 4 binary subgroup failure modes, stored in columns. The main failure rate is about 5.0 % for the whole dataset. The features that constitute this dataset are as follows:
Process time t (s): This feature captures the full duration of each drilling operation, providing insights into efficiency and potential bottlenecks.
Main failure: This binary feature indicates if any significant failure on the drill bit occurred during the drilling process. A value of 1 flags a drilling process that encountered issues, which in this case is true when any of the subgroup failure modes are 1, while 0 indicates a successful drilling operation without any major failures.
Subgroup failures: - Build-up edge failure (215x): Represented as a binary feature, a build-up edge failure indicates the occurrence of material accumulation on the cutting edge of the drill bit due to a combination of low cutting speeds and insufficient cooling. A value of 1 signifies the presence of this failure mode, while 0 denotes its absence. - Compression chips failure (344x): This binary feature captures the formation of compressed chips during drilling, resulting from the factors high feed rate, inadequate cooling and using an incompatible drill bit. A value of 1 indicates the occurrence of at least two of the three factors above, while 0 suggests a smooth drilling operation without compression chips. - Flank wear failure (278x): A binary feature representing the wear of the drill bit's flank due to a combination of high feed rates and low cutting speeds. A value of 1 indicates significant flank wear, affecting the drilling operation's accuracy and efficiency, while 0 denotes a wear-free operation. - Wrong drill bit failure (300x): As a binary feature, it indicates the use of an inappropriate drill bit for the material being drilled. A value of 1 signifies a mismatch, leading to potential drilling issues, while 0 indicates the correct drill bit usage.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Hybrid feature selection algorithm is a strategy that combines different feature selection methods aiming to overcome the limitations of a single feature selection method and improve the effectiveness and performance of feature selection. In this paper, we propose a new hybrid feature selection algorithm, to be named as Tandem Maximum Kendall Minimum Chi-Square and ReliefF Improved Grey Wolf Optimization algorithm (TMKMCRIGWO). The algorithm consists of two stages: First, the original features are filtered and ranked using the bivariate filter algorithm Maximum Kendall Minimum Chi-Square (MKMC) to form a subset of candidate features S1; Subsequently, S1 features are filtered and sorted to form a candidate feature subset S2 by using ReliefF in tandem, and finally S2 is used in the wrapper algorithm to select the optimal subset. In particular, the wrapper algorithm is an improved Grey Wolf Optimization (IGWO) algorithm based on random disturbance factors, while the parameters are adjusted to vary randomly to make the population variations rich in diversity. Hybrid algorithms formed by combining filter algorithms with wrapper algorithms in tandem show better performance and results than single algorithms in solving complex problems. Three sets of comparison experiments were conducted to demonstrate the superiority of this algorithm over the others. The experimental results show that the average classification accuracy of the TMKMCRIGWO algorithm is at least 0.1% higher than the other algorithms on 20 datasets, and the average value of the dimension reduction rate (DRR) reaches 24.76%. The DRR reached 41.04% for 12 low-dimensional datasets and 0.33% for 8 high-dimensional datasets. It also shows that the algorithm improves the generalization ability and performance of the model.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Project Documentation: Cucumber Disease Detection
Introduction: A machine learning model for the automatic detection of diseases in cucumber plants is to be developed as part of the "Cucumber Disease Detection" project. This research is crucial because it tackles the issue of early disease identification in agriculture, which can increase crop yield and cut down on financial losses. To train and test the model, we use a dataset of pictures of cucumber plants.
Importance: Early disease diagnosis helps minimize crop losses, stop the spread of diseases, and better allocate resources in farming. Agriculture is a real-world application of this concept.
Goals and Objectives: Develop a machine learning model to classify cucumber plant images into healthy and diseased categories. Achieve a high level of accuracy in disease detection. Provide a tool for farmers to detect diseases early and take appropriate action.
Data Collection: Using cameras and smartphones, images from agricultural areas were gathered.
Data Preprocessing: Data cleaning to remove irrelevant or corrupted images. Handling missing values, if any, in the dataset. Removing outliers that may negatively impact model training. Data augmentation techniques applied to increase dataset diversity.
Exploratory Data Analysis (EDA) The dataset was examined using visuals like scatter plots and histograms. The data was examined for patterns, trends, and correlations. Understanding the distribution of photos of healthy and ill plants was made easier by EDA.
Methodology Machine Learning Algorithms:
Convolutional Neural Networks (CNNs) were chosen for image classification due to their effectiveness in handling image data. Transfer learning using pre-trained models such as ResNet or MobileNet may be considered. Train-Test Split:
The dataset was split into training and testing sets with a suitable ratio. Cross-validation may be used to assess model performance robustly.
Model Development The CNN model's architecture consists of layers, units, and activation operations. On the basis of experimentation, hyperparameters including learning rate, batch size, and optimizer were chosen. To avoid overfitting, regularization methods like dropout and L2 regularization were used.
Model Training During training, the model was fed the prepared dataset across a number of epochs. The loss function was minimized using an optimization method. To ensure convergence, early halting and model checkpoints were used.
Model Evaluation Evaluation Metrics:
Accuracy, precision, recall, F1-score, and confusion matrix were used to assess model performance. Results were computed for both training and test datasets. Performance Discussion:
The model's performance was analyzed in the context of disease detection in cucumber plants. Strengths and weaknesses of the model were identified.
Results and Discussion Key project findings include model performance and disease detection precision. a comparison of the many models employed, showing the benefits and drawbacks of each. challenges that were faced throughout the project and the methods used to solve them.
Conclusion recap of the project's key learnings. the project's importance to early disease detection in agriculture should be highlighted. Future enhancements and potential research directions are suggested.
References Library: Pillow,Roboflow,YELO,Sklearn,matplotlib Datasets:https://data.mendeley.com/datasets/y6d3z6f8z9/1
Code Repository https://universe.roboflow.com/hakuna-matata/cdd-g8a6g
Rafiur Rahman Rafit EWU 2018-3-60-111
This paper describes the methods used to obtain the spectroscopic data and construct redshift catalogs for the Canada-France Deep Redshift Survey (CFRS). The full data set consists of more than 1000 spectra, of objects with 17.5=<I_AB_=<22.5, obtained from deep multislit data with the MARLIN and MOS-SIS spectrographs at the Canada-France-Hawaii Telescope (CFHT). The final spectroscopic catalog contains 200 stars, 591 galaxies with secure redshifts in the range 0=<z=<1.3, six QSOs, and 146 objects with very uncertain or unknown redshifts, leading to an overall success rate of identification of 85%. In addition, 67 objects affected by observational problems have been placed in a supplemental list. We describe here the instrumental setup and the observing procedures used to gather this large data set efficiently. New optimal ways of packing spectra on the detector to increase significantly the multiplexing gain offered by multislit spectroscopy are described. Dedicated data reduction procedures have been developed under the IRAF environment to allow for fast and accurate processing. Very strict procedures have been followed to establish a reliable list of final spectroscopic measurements. Fully independent processing of the data has been carried out by three members of the team for each data set associated with a multislit mask, and final redshifts were assigned only after the careful comparison of the three independent measurements. A confidence class scheme was established. We strongly emphasize the benefits of such procedures. Finally, we present the spectroscopic data obtained for 303 objects in the 0000-00 and 1000+25 fields. The success rate in spectroscopic identification is 83% for the 0000-00 field and 84% for the 1000+25 field.
Abstract copyright UK Data Service and data collection copyright owner.This study aims to assess the electorate's attitudes towards Britain's economic situation. Main Topics: Attitudinal/Behavioural Questions Satisfaction/dissatisfaction with way Government is running country/Mr. Wilson as Prime Minister/Mr. Heath as Leader of Opposition. Intended vote if there was a General Election, party most inclined to support. Degree of seriousness of Britain's economic problems and who is most to blame (Government/Trade Unions/employers), whether Government is taking the right action. Whether wage rises kept pace with price rises previous year, whether unemployment is likely to increase or decrease in near future, whether would prefer a cut in everyone's living standards or a slight rise in unemployment in order to solve Britain's economic problems. Whether family income is same/more/less than a year ago, whether family is better off/worse off/about the same financially as a year ago, whether it is easier/more difficult/same to make ends meet.Whether the Government should introduce laws to control prices/profits and dividends/wages and salaries, maximum percentage increase in wages that should be allowed during the current year. Knowledge of the Social Contract between the Government and the Trade Unions, and whether it is helping control wage claims/rate of inflation. Main causes of inflation and rising prices (for eg. joining the Common Market, metrication, trade union wage demands, etc.), current rate of inflation. Whether current levels of unemployment are acceptable. Full time employees only: when last had a pay rise, whether feels self as deserving of a rise, minimum percentage rise that would be acceptable to meet needs, whether would be prepared to accept a cut in pay or go without a rise that year. Background Variables Sex, marital status, whether head of household, household composition, age, cohort, social class, television area, age finished full-time education, employment status, registered elector, income, number of electors/ non-electors, occupation of head of household, trade union membership.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The USD/CNY exchange rate fell to 7.1718 on August 22, 2025, down 0.12% from the previous session. Over the past month, the Chinese Yuan has weakened 0.29%, and is down by 0.80% over the last 12 months. Chinese Yuan - values, historical data, forecasts and news - updated on August of 2025.
Not seeing a result you expected?
Learn how you can add new datasets to our index.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.