Programming in Python

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Location Inference for Non-geotagged Tweets in User Timelines

Social media like Twitter have become globally popular in the past decade. Thanks to the high penetration of smartphones, social media users are increasingly going mobile. This trend has contributed to foster various location based services deployed on social media, the success of which heavily depends on the availability and accuracy of users' location information. However, only a very small fraction of tweets in Twitter are geo-tagged. Therefore, it is necessary to infer locations for tweets in order to attain the purpose of those location based services. In this paper, we tackle this problem by scrutinizing Twitter user timelines in a novel fashion. First of all, we split each user's tweet timeline temporally into a number of clusters, each tending to imply a distinct location. Subsequently, we adapt two machine learning models to our setting and design classifiers that classify each tweet cluster into one of the pre-defined location classes at the city level. The Bayes based model focuses on the information gain of words with location implications in the user-generated contents. The convolutional LSTM model treats user-generated contents and their associated locations as sequences and employs bidirectional LSTM and convolution operation to make location inferences. The two models are evaluated on a large set of real Twitter data. The experimental results suggest that our models are effective at inferring locations for non-geotagged tweets and the models outperform the state-of-the-art and alternative approaches significantly in terms of inference accuracy. SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS:

https://towardsdatascience.com/data-smoothing-for-data-science-visualization-the-goldilocks-trio-part-1-867765050615

Making peace with piecewise. We've all heard that information is beautiful. But then life happens, and we find ourselves trying to prettify the mess that often is reality…

Modeling and predicting snow-related power loss is important to economic calculations, load management and system optimization for all scales of photovoltaic (PV) power plants. This paper describes a new method for measuring snow shedding from fielded modules and also describes the application of this method to a commercial scale PV power plant in Vermont with two subsystems , one with modules in portrait orientation and the other in landscape. The method relies on time-series images taken at 5 minute intervals to capture the dynamics of module-level snow accumulation and shedding. Module-level images extracted from the full-field view are binarized into snow and clear areas, allowing for the quantification of percentage snow coverage, estimation of resulting module power output, and temporal changes in snow coverage. Preliminary data from the Vermont case study suggests that framed modules in portrait orientation outperform their framed counterparts in landscape orientation by as much as 24% energy yield during a single shedding event. While these data reflect a single event, and do not capture snow shedding behavior across diverse temperature and other climatic conditions, the study nonetheless demonstrates that 1) module orientation and position in the array influence shedding patterns; 2) the start of power production and bypass diode activation differ for portrait and landscape module orientations at similar percentages and orientations of snow coverage; and 3) system design is an important factor in snow mitigation and increased system efficiency in snowy climates.

Millions of people utilise social networking services all over the world. Users' interactions with social media sites like Twitter and Facebook have a huge impact on daily life, with some unfavourable consequences. Spammers have converted popular social networking sites into a target platform for disseminating a large number of irrelevant and harmful information. Twitter, for example, has grown to be one of the most widely utilised platforms of all time, allowing for an excessive amount of spam. Fake users send unwanted tweets to users in order to advertise services or websites, which not only harm actual users but also waste resources. Furthermore, the ability of spreading false information to users via fake identities has grown, resulting in the spread of hazardous materials. The detection of spammers and the identification of fraudulent users on Twitter has recently been a popular research topic in today's online social networks (OSNs).

A simplicidade de Python permite que você se torne produtivo rapidamente, porém isso muitas vezes significa que você não estará usando tudo que ela tem a oferecer. Com este guia prático, você aprenderá a escrever um código Python eficiente e idiomático aproveitando seus melhores recursos – alguns deles, pouco conhecidos. O autor Luciano Ramalho apresenta os recursos essenciais da linguagem e bibliotecas de Python mostrando como você pode tornar o seu código mais conciso, mais rápido e mais legível ao mesmo tempo.

Muitos programadores experientes tentam dobrar o Python para que ele se enquadre em padrões aprendidos com outras linguagens e jamais descobrem os recursos do Python que estão além de sua experiência. Com este livro, esses programadores Python aprenderão a ser totalmente proficientes em Python 3.

The existing diabetes analysis techniques are considering few
parameters like age, sex, BMI, insulin, glucose, blood pressure, diabetes pedigree function, pregnancies. But in this paper, we considered in addition to the basic parameters, we also included serum creatinine, potassium, GlasgowComaScale, heart rate/pulse rate, respiration rate, body temperature, lowdensity lipoprotein (LDL), high-density lipoprotein (HDL), TG (Triglycerides).
Our paper includes analysis of Pima Indian diabetes datasets which are available in UC Irvine (UCI) machine learning repository, the data set which was acquired from a hospital in Frankfurt, Germany, and also visited some local hospitals to get Data sets for a diabetes analysis. Since the analysis includes all parameters which cause diabetes, it may also help to detect diseases like heart disease, neuropathy, retinopathy, hearing loss, and dementia. The main aim of this research work is to analyze the datasets by using different machine learning algorithms along with parameter tuning. The proposed research work also analyzes diabetes risk factors and based on the risk factor, it provides suggestions to the patients. The importance of this paper is continuously monitoring the model if model accuracy reduces, it automatically rebuilds the model and suggests the best model. We prepared the Flask API (Application Programming
Interface) to consume this model, which is primarily integrated into the frontend. So that if a user calls this API it will display the diabetes status of the patients. We have collected live samples of datasets from apple phone readings and reached many hospitals for live data sets and in each time if the accuracy reduces in the current model it will automatically reload and considers the model that produces better accuracy.

My project Hospital Database Management system includes registration of patients, storing their disease details into the system. It will also contain doctor's information and will digitalize the whole billing system. It has the facility to give a unique id for every patient and stores the details of every patient and staff automatically. It includes a search facility to know the current status of each room. User can search availability of a doctor and the details of a patient using the id. And the whole process conducted by Administrator.

Arada programlama/kodlama pratikleri yapmak diğer alanlardaki çalışmalara da pozitif etki ediyor. Zihninizdekini programa koda aktarıp çalıştırmak, çalıştığını görmek güzel.
          Python programlama dili ve bilim paketi eklentileri Numpy(numeric python), Scipy(scientific python), Matplotlib (bilimsel fonksiyonların 2 ve 3 boyutta çizimi) ve Pandas çok büyük bir okyanus..
          İstanbul’a kurulması bir gereklilik ihtiyaç olan  teknik kökenli “Yazıtbilim ve Arkeoloji Araştırma Enstitüsü” Projesi 4ncü versiyonu öncesi yoğun bir Python kodlama çalışmam olmuştu.
            Birkaç gündür Python ‘a tekrar yoğunlaşmanın ardından , önceki notlardan kod pratiklerinden oluşan 6 pdf  1200 sayfayı geçip  kitap formatına gelmeye başladı.
            Bu çalışmalarımın İkinci cildin “Python Kodlama II: Numpy(Numeric Python), Scipy(Scientific Python),  Matplotlib ve Pandas Science/Bilim Paketi” nden  4-5 günde oluşturduğum faydalı bir çalışma.
          “Python Programlama Dilinde  3 Boyutlu Vektör Alanı  Çizdirmek. Akışkanlar Mekaniği ‘ndeki Hız Vektör Alanının Çizdirilmesi Örneği”
          Öğrenciler öğrenmek isteyenler faydalanır kalıcı bilgi olsun diye arada bunu da yayınlamış  olduk.
          Vektör alanları  uçakların, helikopterlerin, uzay mekiğinin tasarımında  kullanılan akışkanlar mekaniğinde/fluid mechanics  aerodinamik de, elektomanyetik alanlarda, mühendislikteki tüm makinaların ve inşaattaki mukavemet/dayanım hesabında, ısı transferinde/heat transfer, kütlesel çekimde yerçekimi ve gezegenlerin çekim alanında /gravitation da karşımıza çıkmakta.
          Asıl , İstanbul’a kurulması bir gereklilik olan teknik kökenli Yazıtbilim ve Arkeoloji  Araştırma Enstitü Projesi ve İstanbul Arkeolojik keşfi buluntuları , oluşturduğum ve tamamlamak üzere olduğum kitapları ve hazırladığım ilgili makaleler üzerinde çalıştığımdan, toplamda 1200 küsür sayfayı aşan Python pratiklerim ve notlarımdan şimdilik bu kadarını yayınlıyorum.

Python is universal programming language used for information science and machine learning calculations. machine learning calculations gives  processing method to Python and its libraries like numpy, scipy, pandas, matplotlib. And clarifies how it tends to connect to create machine learning calculations. Take care of certifiable issues. In the first place it explains the Importance of Machine learning Using Python.

Coding Elements is creating the next generation of coders who will change their cities, their countries, and the world. Coding Elements curates the best curriculum in high-growth areas such as machine learning, data science, and full-stack development - with input from the industry. The online and classroom courses offered by Coding Elements have been rated favorably by thousands of students and have helped hundreds of students secure meaningful jobs.

Python is a widely used high-level, general-purpose, interpreted, dynamic programming language. Its design philosophy emphasizes
code readability, and its syntax allows programmers to express concepts in fewer lines of code than would be possible in
languages such as C++ or Java. The language provides constructs intended to enable clear programs on both a small and large
scale.

Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural
styles. It features a dynamic type system and automatic memory management and has a large and comprehensive standard
library. (Source: https://en.wikipedia.org/wiki/Python_%28programming_language%29)
In this ebook, we provide a compilation of Python examples that will help you kick-start your own projects. We cover a wide
range of topics, from multi-threaded programming to web development with Django. With our straightforward tutorials, you will
be able to get your own projects up and running in minimum time.

In finance, the binomial options pricing model provides a generalizable numerical method for the valuation of options. Essentially, the model uses a "discrete-time" (lattice based) model of the varying price over time of the underlying financial instrument, addressing cases where the closed-form Black-Scholes formula is wanting. The binomial model was first proposed by William Sharpe in the 1978 and was formalized by Cox, Ross and Rubinstein (CR) in 1979 and by Rendleman and Bartter in that same year. This paper will calculate the value of the range of options under the CRR model using Python. It will then investigate the pricing accuracy and convergence of the algorithm. We will then investigate other methods to increase the efficiency, convergence, and stability of the method.

1 Chuyển đổi văn bản thành giọng nói dùng thư viện Python pyttsx3 (Text to Speech TTS) https://github.com/nateshmbhat/pyttsx3 Pyttsx3 là phần mềm chuyển đổi văn bản thành giọng nói trực tiếp không cần nối mạng và không cần lưu tiếng nói vào bộ nhớ. Sử dụng thư viện tổng hợp giọng nói (Speech Synthesis) có sẵn trên hệ điều hành máy tính như sapi5 (windows), nsss (OS Mac), espeak (android, ubuntu), có mã nguồn trên github. Google có gTTS nhưng đòi hỏi phải nối mạng. Nhiều công ty như FPT, Viettel cũng sản xuất các phần mềm TTS có trả phí. Ngoài ra còn có nhiều app trên smartphone hỗ trợ phát âm cho các file phục vụ cho người khiếm thính hay đang bận rộn chỉ muốn nghe không có thì giò đọc. Trên máy tính cũng có một số phần mềm như Foxit Reader , Microsoft Office 2019 hỗ trợ phát âm file tiếng Anh Nếu bạn muốn thử mày mò viết một chương trình đọc tiếng Việt riêng cho mình? Hay bạn muốn viết một chương trình cho robot phát âm tiếng Việt dùng Raspberry Pi? Phần tiếp theo sử dụng Python trên hệ điều hành Windows Cài đặt: pip install pyttsx3 1/Chạy thử: import pyttsx3 import os engine = pyttsx3.init() #hay pyttsx3.init(driverName='sapi5') engine.say("Good morning, How are you") engine.say("Today is 12 February") engine.save_to_file("Hello" , "chao.mp3") # Flush the say() queue and play the audio engine.runAndWait() os.system("chao.mp3") 2/Cài đặt nhịp điệu và âm lượng engine.setProperty('rate', 50) engine.setProperty('volume', 0.25) 3/Giọng nói và ngôn ngữ Vào Settings Time & Language Language cài đặt ngôn ngữ English, Vietnames… Vào Settings Time & Language Speech cài đặt giọng nói Voices Add Voices chọn Vietnamese

This short Winter course at the VU Amsterdam is very interesting for master students, PhD candidates and professionals who are interested in learning Python, especially for analysing texts with methods of automated content analysis. The course covers the basics of Python programming and gives insights into sentiment analysis, opinion mining, (un-) supervised machine learning, and data extraction through web scraping. This course will especially interest the once who desire to fully engage themselves within Python programming opportunities, and who desire to learn how to overcome the challenges presented within organizing data that simply cannot be comprehended using traditional methods.

The present work aims to classify the genre of the books automatically using the Python programming language. A genre is a subset of art, literature, or music that has a distinct form, substance, and style. In many instances, a book can be classified as belonging to more than one genre. It's difficult to categorize a book or piece of literature as belonging to one genre over another. Many novels end up badly categorized or pushed under the super-genre umbrella of fiction since there is no clear criterion to determine how much of a book belongs to a given genre. Therefore, it's critical to develop a system for categorizing books and determining their relevance to a particular genre. Therefore, the current study tries to solve this challenge by combining various text categorization approaches and models to come up with the best solution. I.

This study aims to create an ima the synogram of scanning perfume bottles on a obtained from the collection of perfume bottle scanning results. The synogram will then be reconstructed in Python to get the reconstructed image. The test was performed visually and numerically which included speed, spatial resolution and reconstruction program can be made. This program can be used to reconstruct the results of the scanning process synograms on the Computed Tomography reconstructed using Python is better than the reconstructed image quality using Matlab. However, judging from the time required for the reconstruction process, the the reconstruction process using Matlab. Tomography is a technology used to see the inner cross section (doing 'splitting') of an object without having to split the object in question. Various modes of tomography technology that have been developed in the medical world include CT scan (nuclear radiation), MRI (ultra high magnetic field), PET (nuclear particle radiation), ultrasonography (ultrasonic), etc. information, but also carries potential dangers due to th get the lowest possible dose while maintaining image quality. Since CT images were first used in clinical settings, standard methods of image reconstruction have been applied to make objects shar

A word cloud is a kind of weighted list to visualize language or text data, which gains increasing attention and more application opportunities as the big data time approaches. Currently, there has been some online word cloud generators available for users with simple requests, such as repeating the exact phrase, or collecting the text data from a web page. Moreover, most current word cloud generators cannot support characters other than English, which are limited in English-speaking users. There are also packages for programming languages (such as Python and R) to generate word clouds, which requires coding and is not userfriendly. This paper is focusing on developing a graphical user interface (GUI) software to generate word cloud maps with easy operations. The Python programming language is involved and some details are discussed. Finally the software is released for further application

Undergraduate Topics in Computer Science
Series Editor
Ian Mackie, University of Sussex, Brighton, UK
Advisory Editors
Samson Abramsky, Department of Computer Science, University of Oxford,
Oxford, UK
Chris Hankin, Department of Computing, Imperial College London, London, UK
Dexter C. Kozen, Computer Science Department, Cornell University, Ithaca, NY,
USA
Andrew Pitts, University of Cambridge, Cambridge, UK
Hanne Riis Nielson , Department of Applied Mathematics and Computer Science,
Technical University of Denmark, Kongens Lyngby, Denmark
Steven S. Skiena, Department of Computer Science, Stony Brook University, Stony
Brook, NY, USA
Iain Stewart, Department of Computer Science, Science Labs, University of
Durham, Durham, UK
Mike Hinchey, University of Limerick, Limerick, Ireland

5 sene önce İstanbul’da keşfettiğim ve insanlığın en eski eserlerini barındıran paleolitik kültürün buluntuları üzerinde çalışırken ve ilgili 17 makaleyi yazıp yayınlarken teknik konularda alanda çok büyük boşluk olduğunu görüp, İstanbul’da kurulmak üzere teknik kökenli bir Yazıt Bilim ve Arkeolji Araştırma Enstitüsü’nün projesini hazırlamaya başladım. Bu Enstitü projesini hazırlarken ders programlarının detaylarında Python’a dalınca ve 7-8 adet defter kitap şeklinde çalışır programlardan, örnek programlardan ve notlarımdan oluşan dijital not defterleri oluştu. Bu Python çalışmalarının bir kısmı olan “Python VTK Patraview ile Bilimsel Görselleştirme” başlıklı kitabı “Türkçe kaynak yok, dijital rafda kalmasın üniversite öğrencilerine akademisyenlere kaynak olsun” deyip ISBN de alıp şahıs yayıncı olarak dijital olarak pdf olarak Resersarchgate ve academia.edu ‘da Eylül 2018’de  yayınladım.

          Bu “Python ile Bilimsel Programla” ise İngilizce çok kaynak olduğu acelesi olmadığı için  bekledi. Evet İngilizce kaynak çok ama böyle düzenli hepsi el altında olan ansikkooedik tarz bir kitabın değeri başka.
            Kitabın içindeki modüller Numpy, Scipy, Matplotlib ve Pandas Python un bilim(Science) paketi.  Numpy(Numeric/Sayısal Python)  Matpotlib(grafik çizim kütüphanesi) ile birlikde bugüne kadar üniversitelerde çok geniş kullanımı olan Matlab’e alternatif. Numpy matris cebri  lineer cebir konularında en az Matlab kadar etkin.
            Kitabın içindeki konuların hitap ettiği kesim Lise matematiği ile başlar. Üniversite yüksek matematik ve uygulama alanları  ile devam eder. 
          Yapay zekada(Artificial Intelligence),  Veri Biliminde(Data Science) ve Makine Öğrenmesi’nde(Machine Learning) gibi  konularda da  Numpy’nin Numeric Python’un,  Veri Analizi(Data Analysis) modülü Pandas ile birlikte kullanımı olduğunu da belirtelim.
            Python Numpy Pandas modüllerinin yüklemelerininin nasıl yapılacağı bilgisini  de, bilgisayarınızda hiç Python yokmuş gibi düşünerek , kitabın başında anlatıyoruz.
            Matpoltlib çizim modülünü anlattığımız kısma eklediğimiz dağılım (scatter) çizimi kısmı, ilişkilendirme(regression), kümeleme (clustering) gibi konuları ile, Python'a eklenen Seaborn istatiksel grafik çizimi modülü ve Scikit-learn makina öğrenmesi modülü ile makina öğrenmesi konusuna doğru devam eder.
                           
      Bu kitaptaki bilim paketindeki modüller Numpy(Numeric Python), SciPy(Scientific Python), Matplotlib, Pandas,  birçok alanın da kökeni. Örneğin makine öğrenmesinin ,örneğin Veri Bilimi(Data Science) ın, hatta robotiğin. Teknikte ve bilimde birçok alan bu modüllere eklenen yeni modüller(kütüphaneler) ile  devam ediyor.
          Türkçe bir kaynak olarak, birçok komutun örnekleri ile bir arada bulunduğu ansiklopedi tarzı bu Python Bilim Paketinin lise öğrencilerinden üniversite, yüksek lisans, doktora öğrencilerine kadar, hatta profesörlerine kadar veya meslekte olup da öğrenme gereksinimi olanlara kadar geniş bir kesime faydalı olması dileği ile.

Undergraduate Topics in Computer Science
Series Editor
Ian Mackie, University of Sussex, Brighton, UK
Advisory Editors
Samson Abramsky, Department of Computer Science, University of Oxford,
Oxford, UK
Chris Hankin, Department of Computing, Imperial College London, London, UK
Dexter C. Kozen, Department of Computer Science, Cornell University, Ithaca, NY,
USA
Andrew Pitts, University of Cambridge, Cambridge, UK
Hanne Riis Nielson , Department of Applied Mathematics and Computer Science,
Technical University of Denmark, Kongens Lyngby, Denmark
Steven S. Skiena, Department of Computer Science, Stony Brook University,
Stony Brook, NY, USA
Iain Stewart, Department of Computer Science, Science Labs, University of
Durham, Durham, UK
Mike Hinchey, University of Limerick, Limerick, Ireland

Buku pembelajaran tentang Tips Cerdas Bersama Python merupakan buku panduan serta buku pegangan bagi seseorang yang ingin belajar bahasa pemrograman Phyton dari awal, dikemas secara lengkap dan redaksi kata yang mudah dipahami, serta diberikan penekanan pada pengimplementasian program-program sederhana agar pembaca dapat memahami alu-alur berjalannya suatu program tertentu. Python dipilih karena relatif mudah dipelajari terutama bagi orang yang baru mengenal bahasa pemrograman pada komputer.

DETECCIÓN DE CONTORNOS EN IMÁGENES CON PYTHON Y "opencv". El propósito del presente trabajo, es exponer el procedimiento a seguir, para la detección de contornos sobre una imagen con el lenguaje de programación "Python", haciendo uso de la librería "opencv". Así, para empezar, procederemos a importar las librerías y recursos que vamos a emplear en nuestro proyecto: >>> #IMPORTAMOS LIBRERÍAS A USAR. >>> import cv2 >>> import matplotlib.pyplot as plt >>> Haciendo uso conjunto de estas dos librerías, nos disponemos a representar los contornos de una imagen sobre fondo blanco, contenida en una archivo de nombre "hand.png":

Web app authentication using face detection is a blended project related to artificial intelligence. A framework from python like Django and OpenCV are used
(Keywords:  Facial Recognition, Django, OpenCV, Artificial Neural Network, Inception Block, TensorFlow, Keras , NumPy, imutils, SciPy)