Data-driven-Ship-Fuel-Efficiency-Modeling

This is my Python project named QQTPVE, which adopts the logics and resources of Tencent QQTang. I am a beginner of Python, so my code is messy. I sincerely recommend that you never dig into it, or your soul will be badly polluted, haha!

Sri Venkateshwara University (SVU) strives to create professionals who are not only adept in academics but also in application for the benefit of humanity. We foster a culture of learning by doing. We believe in nurturing students who are at the forefront of innovation by offering an environment of research & development to make us Best University in Uttar Pradesh (UP). SVU believes in experiential learning. To facilitate this, we have an ultra-modern infrastructure that motivates students to experiment & excel in their area of interest. The Best University of Moradabad has laboratories & workshops that signify our commitment to core research, thus enabling innovation. SVU is the only institution to have set up labs in collaboration with the industry. This way we can train our students on the latest skills & make them employable. Students sharpen their practical skills under the watch full eyes of trainers & become competent professionals. For the overall development of the students, we organize cultural programs. Students take part in these programs & exhibit their talent to become confident professionals. The annual fest attracts students from all over the country & showcase their talent to make us the Top University in India. We equipped the computing labs with the latest software & hardware to augment the technical skills of the students. SVU’s library is an epitome of knowledge. It has over 3000 books & journals that ensure the students are never short on intellectual input. The team of industry trainers educate them on the key skills so crucial for employment & make us the Best University in Gajraula. The specially created engineering labs assist engineers to refine their technical acumen so much needed for the country. The Chairman Dr. Sudhir Giri believes in removing all the economic & social barriers that can hinder education. Hence, SVU provides many scholarships & grants to meritorious students. Up till now, the college has enabled over 500000 students to attain their academic desires to make us the Best Private University in Uttar Pradesh (UP). The group is running a dozen educational institutions that include medical colleges in India & abroad. Our commitment towards education & healthcare has enabled Dr Sudhir Giri to win the International Glory Man of the year Award 2021. The Best Private University in Moradabad is on the Delhi Moradabad highway, well connected with rail & road. The green surroundings provide peace of mind that enables research based learning. The carefully recruited faculty is the pride of the university. They have years of industrial & academic experience so vital for the students. They transfer key skills & make us the Best Private University in Gajraula. The faculty encourages students to undertake research & sharpen their skills that will enable them to get jobs. Majority of the faculty members are doctorates who educate the students to become competent professionals. The faculty takes part in FDP in order to develop a culture of research. The specialty of SVU is the internship. We have partnered with leading industries for providing internship to the students. We believe that education without applicability is incomplete. Students gain hands on exposure through internship & become job ready. We place most of the students during internship to make us the Top University in India. SVU, the Best University in Uttar Pradesh (UP), adopts a futuristic teaching pedagogy. We strive for experiential learning of our students through role plays, projects & presentation. The students take part in the learning activity & imbibe concepts that enable their placements. The AC seminar & conference halls allow knowledge dispersion for the development of the students. The University is running over 150 undergraduate (UG), postgraduate (PG) courses, (Ph.D.), diploma and certificate courses in various fields of Applied Sciences, Medical Science, Humanities & Social Sciences. We also run courses in Languages, Design, Agriculture, Engineering & Technology, Nursing, Pharmacy, Paramedical, Commerce & Management, Law, Library & information Sciences, Mass Comm. & Journalism to enhance the employability of the youth. SVU has a culture of project based learning. Students do projects in each semester under the guidance of faculty. They complete these projects in earmarked industries to garner hands-on skills. Through these projects, we train students on the hot skills so crucial for employment to make us the Best University in Moradabad. SVU’s Research & Development (R&D) wing encourages students to work on research areas important for the country. We have partnered with leading research institutions to undertake research. The breath-taking infrastructure of the best university in Gajraula motivates researchers to achieve their goals for research. Owing to our dedication, SVU has received grants from GOI for research on areas of national importance. The faculty members provide guidance to the scholars until they achieve their aim. We have set up the incubation center to provide fillip to new ideas that foster entrepreneurship. We want to be an institution that supports the ‘Make in India’ vision of the government. The center supports new ideas that enable the young entrepreneurs to create startups & become successful. Under the strong leadership of Dr. Sudhir Giri, till date we have successfully incubated 150 start-ups. This speaks of our exemplary education & make us the Best Private University in Uttar Pradesh (UP). These startups are not only creating wealth but also providing employment to the needy. The industrialists have lamented that the epicenter for entrepreneurship will be the educational institutions. We need to provide them with the support & infrastructure for this. The annual hackathon attracts individuals who showcase their business acumen to make us the Best Private University in Moradabad. SVU has a dedicated International Research & collaboration Cell (IRCC) that collaborates with universities abroad. Faculty & students who want to pursue studies abroad the IRCC starts admission formalities for them. We have partnered with reputed institutions for providing excellent research collaborations. Those who wish to do P. HD abroad the IRCC help them gain admission & make us the Top University in India. A lot of our faculty members are pursuing their research internationally & contributing to the welfare of humanity. SVU strives to make our students feel comfortable at the campus. Separate hostel for boys & girls with 24 hour security is available at SVU. The cafeteria serves nutritious food to the students. Gym, recreation hall & the sports ground help to relax our students & make us the Best University in Uttar Pradesh (UP). The campus has an in house ATM & convenience store for the benefit of the students. SVU enables placement through exemplary training. We train on communication & interpersonal skills in order to refine the personality of the students. We make them practice mock interviews & group discussion that help to clear placement tests. Ninety percent of the students get placed before their last semester to make us the best university in Moradabad. We have hired industrial trainers in order to provide training on block chain, machine learning, artificial intelligence (AI), and python & data science. These trainers have years of experience that enables them in training the students. The students gain key insights on these technologies & sharpen their acumen to make us the Best University in Gajraula.

It performs Facial recognition with high accuracy. This attendance project uses webcam to detect faces and records the attendance live in an excel sheet. In order to determine the distinctive aspects of the faces based on distance, convolutional neural networks are used. All you need to do is stand in front of the camera and your face is verified instantly in milliseconds, without recording the attendance more than once. Facial recognition systems are commonly used for verification and security purposes but the levels of accuracy are still being improved. Errors occurring in facial feature detection due to occlusions, pose and illumination changes can be compensated by the use of hog descriptors. The most reliable way to measure a face is by employing deep learning techniques. The final step is to train a classifier that can take in the measurements from a new test image and tells which known person is the closest match. A python based application is being developed to recognize faces in all conditions. We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.

Object Detection in general means machines method to locate and label objects. These techniques can be used for both static and dynamic data. These techniques are already in wide use in many industries such as smart phones, sports, smart vehicles. Various methods have been adopted to get the optimal way for recognizing the objects in an image and many of these techniques have a major problem in space management and communication between multiple different platforms. Open source libraries such as OpenCV’s DNN library and tensorflow Object detection API offer easy-to-use, open source frameworks where pre trained models for object detection reaches high accuracy. However the main aim being the space management and versatility of the model among different platforms is the main focus of study here. We have developed an object recognition model using convolutional neural networks that is trained and tested with the CIFAR-10 dataset. The model is based on linear regression as we are using the same to train and test our model. Also since the training process is complex, time consuming and space consuming, we will store our training data in a .h5 file. Then we can use the same file for the same model or a different model using the same training data as a part of multiple models used in the detector. In this project TensorFlow and Keras API are used to facilitate the process of building, training and testing the model. Tensor flow is a high level library for numerical computation. It helps us to build machine learning model, however we have to build each layer within our network and manually build training and testing loops and optimizers. It is Flexible and easy to use but keras acts as an API that works on TensorFlow to make all the attributes of a model easily accessible. Keras basically works on top of the other machine learning frameworks. While building our model we will see the difference in accuracy that we will get when we are using different activation functions (such as Tanh, Relu, LeakyRelu, Softmax ) for the same model. The Object recognizer that we are going to built can identify objects from ten different domains that are mentioned in the CIFAR-10 dataset. This dataset contains 60,000 32x32 images, 6000 images in each class. There are 50,000 training images and 10,000 testing images in random order.This model can act as a base for a more complex object recognizer, It can be used as a part of a larger model consisting a large no of models. This model can be used for quick load applications and mobile applications as well. The programming for the following project has been done using python ver.3.5.1. Also the other in built libraries have been used.

Computational results and tests of four formulations for the Optimal Communication Spanning Tree problem. We implemented three formulations: Path-based, flow-based, and rooted-based. Moreover, we consider the relaxation (in some variables) of the flow-based formulation. In this project, we adopt the Gurobi optimizer as the solver for the MILP formulations. The algorithms are implemented using the standard C++ programming language. For analyzing results, we use two Python libraries: Pandas and NumPy. For testing, we use the Google Test c++ framework. Moreover, To generate random instances of the OCST problem, we use the Python Networkx package.

http://martinos.org/mne/ 4,861 commits 15 branches 16 releases 33 contributors Python 99.3% Other 0.6% PythonOther mne-python / Merge pull request #1175 from dgwakeman/write_trans … latest commit 1c606eda60 Alexandre Gramfort agramfort authored 8 hours ago bin FIX: Fix examples 3 months ago doc fix markdown VS rst 8 days ago examples cleanup viz 10 days ago mne COM:round 1 a day ago .coveragerc more coveralls 2 months ago .gitignore WIP mne_kit2fiff: from KIT SQD files to fif files a year ago .mailmap update mailmap 3 months ago .travis.yml FIX: Fix coveralls a month ago AUTHORS.rst FIX: Add Teon 9 months ago LICENSE.txt fix copyrights and authors 2 years ago MANIFEST.in FIX: MANIFEST.in 3 months ago Makefile Allow wget to continue download of a partially downloaded file 22 days ago README.rst Update README.rst 2 months ago dictionary.txt ENH: Use codespell 11 months ago setup.cfg FIX: Undo HTML coverage 2 months ago setup.py WIP: Add mapping 3 months ago README.rst Travis mne-python This package is designed for sensor- and source-space analysis of M-EEG data, including frequency-domain and time-frequency analyses and non-parametric statistics. This package is presently evolving quickly and thanks to the adopted open development environment user contributions can be easily incorporated. Get more information This page only contains bare-bones instructions for installing mne-python. If you're familiar with MNE and you're looking for information on using mne-python specifically, jump right to the mne-python homepage. This website includes a tutorial, helpful examples, and a handy function reference, among other things. If you're unfamiliar with MNE, you can visit the MNE homepage for full user documentation. Get the latest code To get the latest code using git, simply type: git clone git://github.com/mne-tools/mne-python.git If you don't have git installed, you can download a zip or tarball of the latest code: http://github.com/mne-tools/mne-python/archives/master Install mne-python As any Python packages, to install MNE-Python, go in the mne-python source code directory and do: python setup.py install or if you don't have admin access to your python setup (permission denied when install) use: python setup.py install --user You can also install the latest release version with easy_install: easy_install -U mne or with pip: pip install mne --upgrade or for the latest development version (the most up to date): pip install -e git+https://github.com/mne-tools/mne-python#egg=mne-dev --user Dependencies The required dependencies to build the software are python >= 2.6, NumPy >= 1.6, SciPy >= 0.7.2 and matplotlib >= 0.98.4. Some isolated functions require pandas >= 0.7.3 and nitime (multitaper analysis). To run the tests you will also need nose >= 0.10. and the MNE sample dataset (will be downloaded automatically when you run an example ... but be patient) To use NVIDIA CUDA for FFT FIR filtering, you will also need to install the NVIDIA CUDA SDK, pycuda, and scikits.cuda. The difficulty of this varies by platform; consider reading the following site for help getting pycuda to work (typically the most difficult to configure): http://wiki.tiker.net/PyCuda/Installation/ Contribute to mne-python Please see the documentation on the mne-python homepage: http://martinos.org/mne/contributing.html Mailing list http://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis Running the test suite To run the test suite, you need nosetests and the coverage modules. Run the test suite using: nosetests from the root of the project. Making a release and uploading it to PyPI This command is only run by project manager, to make a release, and upload in to PyPI: python setup.py sdist bdist_egg register upload Licensing MNE-Python is BSD-licenced (3 clause): This software is OSI Certified Open Source Software. OSI Certified is a certification mark of the Open Source Initiative. Copyright (c) 2011, authors of MNE-Python All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the names of MNE-Python authors nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission. This software is provided by the copyright holders and contributors "as is" and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall the copyright owner or contributors be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage. http://martinos.org/mne/ http://martinos.org/mne/ 4,861 commits 15 branches 16 releases 33 contributors Python 99.3% Other 0.6% PythonOther mne-python / Merge pull request #1175 from dgwakeman/write_trans … latest commit 1c606eda60 Alexandre Gramfort agramfort authored 8 hours ago bin FIX: Fix examples 3 months ago doc fix markdown VS rst 8 days ago examples cleanup viz 10 days ago mne COM:round 1 a day ago .coveragerc more coveralls 2 months ago .gitignore WIP mne_kit2fiff: from KIT SQD files to fif files a year ago .mailmap update mailmap 3 months ago .travis.yml FIX: Fix coveralls a month ago AUTHORS.rst FIX: Add Teon 9 months ago LICENSE.txt fix copyrights and authors 2 years ago MANIFEST.in FIX: MANIFEST.in 3 months ago Makefile Allow wget to continue download of a partially downloaded file 22 days ago README.rst Update README.rst 2 months ago dictionary.txt ENH: Use codespell 11 months ago setup.cfg FIX: Undo HTML coverage 2 months ago setup.py WIP: Add mapping 3 months ago README.rst Travis mne-python This package is designed for sensor- and source-space analysis of M-EEG data, including frequency-domain and time-frequency analyses and non-parametric statistics. This package is presently evolving quickly and thanks to the adopted open development environment user contributions can be easily incorporated. Get more information This page only contains bare-bones instructions for installing mne-python. If you're familiar with MNE and you're looking for information on using mne-python specifically, jump right to the mne-python homepage. This website includes a tutorial, helpful examples, and a handy function reference, among other things. If you're unfamiliar with MNE, you can visit the MNE homepage for full user documentation. Get the latest code To get the latest code using git, simply type: git clone git://github.com/mne-tools/mne-python.git If you don't have git installed, you can download a zip or tarball of the latest code: http://github.com/mne-tools/mne-python/archives/master Install mne-python As any Python packages, to install MNE-Python, go in the mne-python source code directory and do: python setup.py install or if you don't have admin access to your python setup (permission denied when install) use: python setup.py install --user You can also install the latest release version with easy_install: easy_install -U mne or with pip: pip install mne --upgrade or for the latest development version (the most up to date): pip install -e git+https://github.com/mne-tools/mne-python#egg=mne-dev --user Dependencies The required dependencies to build the software are python >= 2.6, NumPy >= 1.6, SciPy >= 0.7.2 and matplotlib >= 0.98.4. Some isolated functions require pandas >= 0.7.3 and nitime (multitaper analysis). To run the tests you will also need nose >= 0.10. and the MNE sample dataset (will be downloaded automatically when you run an example ... but be patient) To use NVIDIA CUDA for FFT FIR filtering, you will also need to install the NVIDIA CUDA SDK, pycuda, and scikits.cuda. The difficulty of this varies by platform; consider reading the following site for help getting pycuda to work (typically the most difficult to configure): http://wiki.tiker.net/PyCuda/Installation/ Contribute to mne-python Please see the documentation on the mne-python homepage: http://martinos.org/mne/contributing.html Mailing list http://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis Running the test suite To run the test suite, you need nosetests and the coverage modules. Run the test suite using: nosetests from the root of the project. Making a release and uploading it to PyPI This command is only run by project manager, to make a release, and upload in to PyPI: python setup.py sdist bdist_egg register upload Licensing MNE-Python is BSD-licenced (3 clause): This software is OSI Certified Open Source Software. OSI Certified is a certification mark of the Open Source Initiative. Copyright (c) 2011, authors of MNE-Python All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the names of MNE-Python authors nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission. This software is provided by the copyright holders and contributors "as is" and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall the copyright owner or contributors be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage.