Although machine learning (ML) has brought new insights into geochemistry research, its implementation is laborious and time-consuming. Here, we announce Geochemistry pi, an open-source automated ML python framework. ...
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Although machine learning (ML) has brought new insights into geochemistry research, its implementation is laborious and time-consuming. Here, we announce Geochemistry pi, an open-source automated ML python framework. Geochemists only need to provide tabulated data and select the desired options to clean data and run ML algorithms. The process operates in a question-and-answer format, and thus does not require that users have coding experience. After either automatic or manual parameter tuning, the automated python framework provides users with performance and prediction results for the trained ML model. Based on the scikit-learn library, Geochemistry pi has established a customized automated process for implementing classification, regression, dimensionality reduction, and clustering algorithms. The python framework enables extensibility and portability by constructing a hierarchical pipeline architecture that separates data transmission from the algorithm application. The AutoML module is constructed using the Cost-Frugal Optimization and Blended Search Strategy hyperparameter search methods from the A Fast and Lightweight AutoML Library, and the model parameter optimization process is accelerated by the Ray distributed computing framework. The MLflow library is integrated into ML lifecycle management, which allows users to compare multiple trained models at different scales and manage the data and diagrams generated. In addition, the front-end and back-end frameworks are separated to build the web portal, which demonstrates the ML model and data science workflow through a user-friendly web interface. In summary, Geochemistry pi provides a python framework for users and developers to accelerate their data mining efficiency with both online and offline operation options. Geochemistry pi is a helpful tool for scientists who work with geochemical data. One of its standout features is its simplicity. Scientists can use the tool to perform machine learning (ML) on the t
The analysis of time series data, which represents dynamic phenomena through sequences of observations, is greatly influenced by Big Data. Both the sheer volume and the advanced capabilities of Big Data significantly ...
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The analysis of time series data, which represents dynamic phenomena through sequences of observations, is greatly influenced by Big Data. Both the sheer volume and the advanced capabilities of Big Data significantly impact on how these analyses are conducted, enabling more comprehensive and detailed insights. Recent studies have promoted the use of data summarization techniques, for instance through incremental clustering, to address the challenges of Big Data volume. These techniques quickly capture data evolution, thereby helping domain experts make informed and proactive decisions by leveraging a concise representation of time series. However, although incremental clustering efficiently reduces data volume and retains key statistical information, it is important to evaluate the accuracy of the summarized version compared to the original time series data. This assessment is critical when the summarized data is used as the basis for complex analytical pipelines, such as those for pattern recognition and anomaly detection. Moved by these premises and starting from an empirical experience on the definition of a metric to assess the adherence of summarised time series to the original data stream, in this paper: (i) we propose a variant of a renowned quality metric for incremental clustering based on an abstract model of clustering data structures, to assess the extent to which the time series summary accurately captures the dynamics of the original data;(ii) we present PICTURE (python-based Incremental Clustering for Time series Representation and Evaluation) a framework featuring four widely used incremental clustering algorithms from the literature, equipped with modules for execution, representation, and evaluation of clustering results applied to time series according to the abstract model;(iii) we conduct an extensive qualitative and quantitative analysis of incremental clustering results on a synthetic and two real-world datasets using the PICTURE framework, to
Web scraping is the process of extracting data from a website in an efficient and fast way. In such a scenario, python programming can offer useful set of methods that help web editors to improve the quality of the pr...
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Web scraping is the process of extracting data from a website in an efficient and fast way. In such a scenario, python programming can offer useful set of methods that help web editors to improve the quality of the provided service. This scraper contains three steps 1) to understand the structure of web page, 2) design regular expression pattern and finally use that pattern to get certain data. In this paper, we also used Flask, Request, JSONify library to get the data, after processing, the data is transformed into the JSON form and ready for CSV with help of API. After generated all required regex patterns, the system uses these patterns as a set of rules, and with this, designed scraper tool works efficiently, and achieved outstanding results with help of support libraries to storing and extracting the news and web-based information. The proposedWeb scraping tool eliminates the time and effort of manually collecting or copying data by automating the process. It is found that this designed scraper is easy and direct approach to extract the newspapers, websites, blogs, and images data.
Artificial neural networks, deep learning and machine learning are versatile data-driven tools widely applied in different disciplines such as finance, image and voice recognition, and earth science. For scientists an...
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Artificial neural networks, deep learning and machine learning are versatile data-driven tools widely applied in different disciplines such as finance, image and voice recognition, and earth science. For scientists and enthusiasts (including those not very experienced with programming), there is a need for easy-to-use and fast-to-setup tools that enable users to prototype and focus on the research part quickly rather than spending time on data preparation, on extracting features and setup multiple experiments for training and validating models. In this paper, we introduce Kit4DL, which is a python package to speed up the experimentation process of machine- and deep-learning by using just a single TOML configuration file, allowing a user to set up all aspects involved in training and validation. Though simple to use in its default mode, the proposed package enables high customisation possibilities for more experienced users. Kit4DL streamlines the deep learning development process by simplifying the creation of the entire training, validation, and testing loop. Users only need to implement a few core methods outlined in a provided configuration file, significantly reducing development time compared to traditional approaches requiring from a user to implement all procedures him/herself. Additionally, Kit4DL facilitates code reusability by allowing researchers to leverage the same codebase across multiple experiments, reducing redundancy and streamlining the experimentation process.
作者：
Lin, QuanyiLu, ShileiYue, LuGuo, TongTianjin Univ
Sch Environm Sci & Engn Tianjin 300072 Peoples R China Tianjin Univ
Tianjin Key Lab Built Environm & Energy Applicat Tianjin Peoples R China TU Berlin
Hermann Rietschel Inst Str 17Juni 135 D-10623 Berlin Germany Tianjin Univ
Sch Environm Sci & Engn 92 Weijin Rd Tianjin 300072 Peoples R China
Decarbonization of district energy systems is essential for China to meet its carbon neutrality goal by 2060. Most existing district energy systems are missing historical load data and have incomplete information, res...
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Decarbonization of district energy systems is essential for China to meet its carbon neutrality goal by 2060. Most existing district energy systems are missing historical load data and have incomplete information, resulting in a lack of data support for the low-carbon transition. Moreover, demand-side load flexibility has not been fully exploited in the planning stage. In this paper, we developed a two-stage computational approach to optimize district loads. We first established an integrated python framework, incorporating the TEASER simulation tool and AixLib model library, to efficiently calculate baseline loads through the bottom-up modeling and simulation of district buildings. Then, a price-based integrated demand response strategy was introduced. A mixed-integer nonlinear programming model was formulated to optimize the energy pricing strategy with the objective of minimum load fluctuations. Finally, a case study was employed to illustrate the feasibility of the calculation method, showing a normalized mean bias error of 7.17%. The results further demonstrated that the strategy could reduce the peak electric and heat loads by 3.55% and 9.57%, and increase load rates by 3.85% and 9.48%, respectively. The strategy could assist district energy service providers to optimize equipment capacity configuration and enhance the low-carbon planning potential of energy systems from the demand-side.
Web scraping is the process of extracting data from a website in an efficient and fast way. In such a scenario, python programming can offer useful set of methods that help web editors to improve the quality of the pr...
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Web scraping is the process of extracting data from a website in an efficient and fast way. In such a scenario, python programming can offer useful set of methods that help web editors to improve the quality of the provided service. This scraper contains three steps 1) to understand the structure of web page, 2) design regular expression pattern and finally use that pattern to get certain data. In this paper, we also used Flask, Request, JSONify library to get the data, after processing, the data is transformed into the JSON form and ready for CSV with help of API. After generated all required regex patterns, the system uses these patterns as a set of rules, and with this, designed scraper tool works efficiently, and achieved outstanding results with help of support libraries to storing and extracting the news and web-based information. The proposed Web scraping tool eliminates the time and effort of manually collecting or copying data by automating the process. It is found that this designed scraper is easy and direct approach to extract the newspapers, websites, blogs, and images data.
The face is one of the simplest ways to distinguish one another's personal image. Face recognition is a personal identification system which uses a person's personal features to recognize the identity of the i...
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ISBN:
(纸本)9781665436564
The face is one of the simplest ways to distinguish one another's personal image. Face recognition is a personal identification system which uses a person's personal features to recognize the identity of the individual. Human facial identification is basically a two-phase procedure, including face detection, where the process is carried out very rapidly in people, whereas the second is the implementation of environments that classify the face as persons, when the eye is positioned within a short distance. Stage is then repeated and established to be one of the most researched biometric strategies and established by experts for facial expression recognition. In this study, we implemented the area of face detection and face recognition image processing MTCNN techniques while utilizing the VGG face model dataset. In this initiative, python framework is the program necessity.
Deep Neural Networks (DNNs) deployment for IoT Edge applications requires strong skills in hardware and software. In this paper, a novel design framework fully automated for Edge applications is proposed to perform su...
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Deep Neural Networks (DNNs) deployment for IoT Edge applications requires strong skills in hardware and software. In this paper, a novel design framework fully automated for Edge applications is proposed to perform such a deployment on System-on-Chips. Based on a high-level python interface that mimics the leading Deep Learning software frameworks, it offers an easy way to implement a hardware-accelerated DNN on an FPGA. To do this, our design methodology covers the three main phases: (a) customization: where the user specifies the optimizations needed on each DNN layer, (b) generation: the framework generates on the Cloud the necessary binaries for both FPGA and software parts, and (c) deployment: the SoC on the Edge receives the resulting files serving to program the FPGA and related python libraries for user applications. Among the study cases, an optimized DNN for the MNIST database can speed up more than 60x a software version on the ZYNQ 7020 SoC and still consume less than 0.43 W. A comparison with the state-of-the-art frameworks demonstrates that our methodology offers the best trade-off between throughput, power consumption, and system cost.
This study tests a number of open source forensic carving tools to determine their viability when run across split raw forensic images (dd) and Expert Witness Compression Format (EWF) images. This is done by carving f...
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ISBN:
(纸本)9781538630662
This study tests a number of open source forensic carving tools to determine their viability when run across split raw forensic images (dd) and Expert Witness Compression Format (EWF) images. This is done by carving files from a raw dd file to determine the baseline before running each tool over the different image types and analysing the results. A framework is then written in python to allow Scalpel to be run across any split dd image, whilst simultaneously concatenating the carved files and sorting by file type. This study tests the framework on a number of scenarios and concludes that this is an effective method of carving files using Scalpel over split dd images.
Now a days everything around the globe is connected via networks like information, places and events which make a tangle of connections. Analyzing social network is to make sense of these complex connections. This wor...
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Now a days everything around the globe is connected via networks like information, places and events which make a tangle of connections. Analyzing social network is to make sense of these complex connections. This work represents the framework to analyze twitter social media tweets using NetworkX and Twitter API. python language tool Ipython/Jupyter is used to examine the networks by applying visual analytic techniques like degree centrality and betweenness centrality to the dataset of twitter hashtags which provides an easier way to analyze the network connections. This framework describes methodology to diagnose each tweet for identification of certain pattern like 'who talk to whom about what' and 'most influential person' in the interconnected/attached network.
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